{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression as LR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "制作用户的信用评分卡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../data/logisitcRegression/rankingcard.csv\")\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA与预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0           1                 1                              0.766127   45   \n",
       "1           2                 0                              0.957151   40   \n",
       "2           3                 0                              0.658180   38   \n",
       "3           4                 0                              0.233810   30   \n",
       "4           5                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 3   age                                   150000 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 5   DebtRatio                             150000 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 11  NumberOfDependents                    146076 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                  0\n",
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime30-59DaysPastDueNotWorse        0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           29731\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime60-89DaysPastDueNotWorse        0\n",
       "NumberOfDependents                       3924\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去除重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 3   age                                   150000 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 5   DebtRatio                             150000 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 11  NumberOfDependents                    150000 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "# 数据删除后恢复索引\n",
    "\n",
    "data.index = range(data.shape[0])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                              0.000000\n",
       "SeriousDlqin2yrs                        0.000000\n",
       "RevolvingUtilizationOfUnsecuredLines    0.000000\n",
       "age                                     0.000000\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.000000\n",
       "DebtRatio                               0.000000\n",
       "MonthlyIncome                           0.198207\n",
       "NumberOfOpenCreditLinesAndLoans         0.000000\n",
       "NumberOfTimes90DaysLate                 0.000000\n",
       "NumberRealEstateLoansOrLines            0.000000\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.000000\n",
       "NumberOfDependents                      0.026160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()/data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 3   age                                   150000 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 5   DebtRatio                             150000 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 11  NumberOfDependents                    150000 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "# 使用均值填补家属人数\n",
    "\n",
    "data[\"NumberOfDependents\"].fillna(int(data[\"NumberOfDependents\"].mean()), inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                              0.000000\n",
       "SeriousDlqin2yrs                        0.000000\n",
       "RevolvingUtilizationOfUnsecuredLines    0.000000\n",
       "age                                     0.000000\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.000000\n",
       "DebtRatio                               0.000000\n",
       "MonthlyIncome                           0.198207\n",
       "NumberOfOpenCreditLinesAndLoans         0.000000\n",
       "NumberOfTimes90DaysLate                 0.000000\n",
       "NumberRealEstateLoansOrLines            0.000000\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.000000\n",
       "NumberOfDependents                      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 1   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 2   age                                   150000 non-null  int64  \n",
      " 3   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 4   DebtRatio                             150000 non-null  float64\n",
      " 5   MonthlyIncome                         120269 non-null  float64\n",
      " 6   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 7   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 8   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 9   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 10  NumberOfDependents                    150000 non-null  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "# 删除无意义的列\n",
    "del data[\"Unnamed: 0\"]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用随机森林填补收入缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor as rfr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_missing_rf(X,y,to_fill):\n",
    "    \"\"\"\n",
    "    使用随机森林填补一个特征的缺失值的函数\n",
    "    参数:\n",
    "    X:要填补的特征矩阵\n",
    "    y:完整的,没有缺失值的标签\n",
    "    to_fill:字符串,要填补的那一列的名称\n",
    "    \"\"\"\n",
    "    #构建我们的新特征矩阵和新标签\n",
    "    df = X.copy()\n",
    "    fill = df.loc[:,to_fill]\n",
    "    df = pd.concat([df.loc[:,df.columns != to_fill],pd.DataFrame(y)],axis=1)\n",
    "    #找出我们的训练集和测试集\n",
    "    Ytrain = fill[fill.notnull()]\n",
    "    Ytest = fill[fill.isnull()]\n",
    "    Xtrain = df.iloc[Ytrain.index,:]\n",
    "    Xtest = df.iloc[Ytest.index,:]\n",
    "    #用随机森林回归来填补缺失值\n",
    "    from sklearn.ensemble import RandomForestRegressor as rfr\n",
    "    rfr = rfr(n_estimators=100)\n",
    "    rfr = rfr.fit(Xtrain, Ytrain)\n",
    "    Ypredict = rfr.predict(Xtest)\n",
    "    return Ypredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.iloc[:, 1:]\n",
    "y = data[\"SeriousDlqin2yrs\"]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fill_missing_rf(x, y, \"MonthlyIncome\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data.loc[:, \"MonthlyIncome\"].isnull(), \"MonthlyIncome\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 1   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 2   age                                   150000 non-null  int64  \n",
      " 3   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 4   DebtRatio                             150000 non-null  float64\n",
      " 5   MonthlyIncome                         150000 non-null  float64\n",
      " 6   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 7   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 8   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 9   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 10  NumberOfDependents                    150000 non-null  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理异常值\n",
    "\n",
    "可以使用箱线图或3σ来查找异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002969</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>0.981278</td>\n",
       "      <td>1.092956</td>\n",
       "      <td>50708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030874</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>1267.000000</td>\n",
       "      <td>4979.040000</td>\n",
       "      <td>329664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>5416.179184</td>\n",
       "      <td>13247.162762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>4402.032353</td>\n",
       "      <td>7400.000000</td>\n",
       "      <td>10770.000000</td>\n",
       "      <td>23250.000000</td>\n",
       "      <td>3008750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.737413</td>\n",
       "      <td>1.107021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count         mean           std  \\\n",
       "SeriousDlqin2yrs                      150000.0     0.066840      0.249746   \n",
       "RevolvingUtilizationOfUnsecuredLines  150000.0     6.048438    249.755371   \n",
       "age                                   150000.0    52.295207     14.771866   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  150000.0     0.421033      4.192781   \n",
       "DebtRatio                             150000.0   353.005076   2037.818523   \n",
       "MonthlyIncome                         150000.0  5416.179184  13247.162762   \n",
       "NumberOfOpenCreditLinesAndLoans       150000.0     8.452760      5.145951   \n",
       "NumberOfTimes90DaysLate               150000.0     0.265973      4.169304   \n",
       "NumberRealEstateLoansOrLines          150000.0     1.018240      1.129771   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  150000.0     0.240387      4.155179   \n",
       "NumberOfDependents                    150000.0     0.737413      1.107021   \n",
       "\n",
       "                                      min    1%        10%          25%  \\\n",
       "SeriousDlqin2yrs                      0.0   0.0   0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines  0.0   0.0   0.002969     0.029867   \n",
       "age                                   0.0  24.0  33.000000    41.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  0.0   0.0   0.000000     0.000000   \n",
       "DebtRatio                             0.0   0.0   0.030874     0.175074   \n",
       "MonthlyIncome                         0.0   0.0   0.170000  1800.000000   \n",
       "NumberOfOpenCreditLinesAndLoans       0.0   0.0   3.000000     5.000000   \n",
       "NumberOfTimes90DaysLate               0.0   0.0   0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines          0.0   0.0   0.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  0.0   0.0   0.000000     0.000000   \n",
       "NumberOfDependents                    0.0   0.0   0.000000     0.000000   \n",
       "\n",
       "                                              50%          75%           90%  \\\n",
       "SeriousDlqin2yrs                         0.000000     0.000000      0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.154181     0.559046      0.981278   \n",
       "age                                     52.000000    63.000000     72.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000     0.000000      1.000000   \n",
       "DebtRatio                                0.366508     0.868254   1267.000000   \n",
       "MonthlyIncome                         4402.032353  7400.000000  10770.000000   \n",
       "NumberOfOpenCreditLinesAndLoans          8.000000    11.000000     15.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000     0.000000      0.000000   \n",
       "NumberRealEstateLoansOrLines             1.000000     2.000000      2.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000     0.000000      0.000000   \n",
       "NumberOfDependents                       0.000000     1.000000      2.000000   \n",
       "\n",
       "                                               99%        max  \n",
       "SeriousDlqin2yrs                          1.000000        1.0  \n",
       "RevolvingUtilizationOfUnsecuredLines      1.092956    50708.0  \n",
       "age                                      87.000000      109.0  \n",
       "NumberOfTime30-59DaysPastDueNotWorse      4.000000       98.0  \n",
       "DebtRatio                              4979.040000   329664.0  \n",
       "MonthlyIncome                         23250.000000  3008750.0  \n",
       "NumberOfOpenCreditLinesAndLoans          24.000000       58.0  \n",
       "NumberOfTimes90DaysLate                   3.000000       98.0  \n",
       "NumberRealEstateLoansOrLines              4.000000       54.0  \n",
       "NumberOfTime60-89DaysPastDueNotWorse      2.000000       98.0  \n",
       "NumberOfDependents                        4.000000       20.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据的描述性统计\n",
    "data.describe([0.01,0.1,0.25,0.5,0.75,0.9,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data[\"age\"] == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除年龄为0的异常值\n",
    "data = data[data[\"age\"] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149999 entries, 0 to 149999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   SeriousDlqin2yrs                      149999 non-null  int64  \n",
      " 1   RevolvingUtilizationOfUnsecuredLines  149999 non-null  float64\n",
      " 2   age                                   149999 non-null  int64  \n",
      " 3   NumberOfTime30-59DaysPastDueNotWorse  149999 non-null  int64  \n",
      " 4   DebtRatio                             149999 non-null  float64\n",
      " 5   MonthlyIncome                         149999 non-null  float64\n",
      " 6   NumberOfOpenCreditLinesAndLoans       149999 non-null  int64  \n",
      " 7   NumberOfTimes90DaysLate               149999 non-null  int64  \n",
      " 8   NumberRealEstateLoansOrLines          149999 non-null  int64  \n",
      " 9   NumberOfTime60-89DaysPastDueNotWorse  149999 non-null  int64  \n",
      " 10  NumberOfDependents                    149999 non-null  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>6.048472</td>\n",
       "      <td>249.756203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002969</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.154176</td>\n",
       "      <td>0.559044</td>\n",
       "      <td>0.981256</td>\n",
       "      <td>1.092958</td>\n",
       "      <td>50708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>52.295555</td>\n",
       "      <td>14.771298</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>0.421029</td>\n",
       "      <td>4.192795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>353.007426</td>\n",
       "      <td>2037.825113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030871</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>0.366503</td>\n",
       "      <td>0.868257</td>\n",
       "      <td>1267.000000</td>\n",
       "      <td>4979.080000</td>\n",
       "      <td>329664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>5416.175292</td>\n",
       "      <td>13247.206834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>4402.000000</td>\n",
       "      <td>7400.000000</td>\n",
       "      <td>10770.000000</td>\n",
       "      <td>23250.000000</td>\n",
       "      <td>3008750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>8.452776</td>\n",
       "      <td>5.145964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>0.265975</td>\n",
       "      <td>4.169318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>1.018233</td>\n",
       "      <td>1.129772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>0.240388</td>\n",
       "      <td>4.155193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>149999.0</td>\n",
       "      <td>0.737405</td>\n",
       "      <td>1.107020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count         mean           std  \\\n",
       "SeriousDlqin2yrs                      149999.0     0.066840      0.249746   \n",
       "RevolvingUtilizationOfUnsecuredLines  149999.0     6.048472    249.756203   \n",
       "age                                   149999.0    52.295555     14.771298   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  149999.0     0.421029      4.192795   \n",
       "DebtRatio                             149999.0   353.007426   2037.825113   \n",
       "MonthlyIncome                         149999.0  5416.175292  13247.206834   \n",
       "NumberOfOpenCreditLinesAndLoans       149999.0     8.452776      5.145964   \n",
       "NumberOfTimes90DaysLate               149999.0     0.265975      4.169318   \n",
       "NumberRealEstateLoansOrLines          149999.0     1.018233      1.129772   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  149999.0     0.240388      4.155193   \n",
       "NumberOfDependents                    149999.0     0.737405      1.107020   \n",
       "\n",
       "                                       min    1%        10%          25%  \\\n",
       "SeriousDlqin2yrs                       0.0   0.0   0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines   0.0   0.0   0.002969     0.029867   \n",
       "age                                   21.0  24.0  33.000000    41.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse   0.0   0.0   0.000000     0.000000   \n",
       "DebtRatio                              0.0   0.0   0.030871     0.175074   \n",
       "MonthlyIncome                          0.0   0.0   0.170000  1800.000000   \n",
       "NumberOfOpenCreditLinesAndLoans        0.0   0.0   3.000000     5.000000   \n",
       "NumberOfTimes90DaysLate                0.0   0.0   0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines           0.0   0.0   0.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse   0.0   0.0   0.000000     0.000000   \n",
       "NumberOfDependents                     0.0   0.0   0.000000     0.000000   \n",
       "\n",
       "                                              50%          75%           90%  \\\n",
       "SeriousDlqin2yrs                         0.000000     0.000000      0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.154176     0.559044      0.981256   \n",
       "age                                     52.000000    63.000000     72.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000     0.000000      1.000000   \n",
       "DebtRatio                                0.366503     0.868257   1267.000000   \n",
       "MonthlyIncome                         4402.000000  7400.000000  10770.000000   \n",
       "NumberOfOpenCreditLinesAndLoans          8.000000    11.000000     15.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000     0.000000      0.000000   \n",
       "NumberRealEstateLoansOrLines             1.000000     2.000000      2.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000     0.000000      0.000000   \n",
       "NumberOfDependents                       0.000000     1.000000      2.000000   \n",
       "\n",
       "                                               99%        max  \n",
       "SeriousDlqin2yrs                          1.000000        1.0  \n",
       "RevolvingUtilizationOfUnsecuredLines      1.092958    50708.0  \n",
       "age                                      87.000000      109.0  \n",
       "NumberOfTime30-59DaysPastDueNotWorse      4.000000       98.0  \n",
       "DebtRatio                              4979.080000   329664.0  \n",
       "MonthlyIncome                         23250.000000  3008750.0  \n",
       "NumberOfOpenCreditLinesAndLoans          24.000000       58.0  \n",
       "NumberOfTimes90DaysLate                   3.000000       98.0  \n",
       "NumberRealEstateLoansOrLines              4.000000       54.0  \n",
       "NumberOfTime60-89DaysPastDueNotWorse      2.000000       98.0  \n",
       "NumberOfDependents                        4.000000       20.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe([0.01,0.1,0.25,0.5,0.75,0.9,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     141661\n",
       "1       5243\n",
       "2       1555\n",
       "3        667\n",
       "4        291\n",
       "98       264\n",
       "5        131\n",
       "6         80\n",
       "7         38\n",
       "8         21\n",
       "9         19\n",
       "10         8\n",
       "11         5\n",
       "96         5\n",
       "13         4\n",
       "12         2\n",
       "14         2\n",
       "15         2\n",
       "17         1\n",
       "Name: NumberOfTimes90DaysLate, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除两年内违约超过30-95天超过98次的记录\n",
    "data.loc[:, \"NumberOfTimes90DaysLate\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.loc[:, \"NumberOfTimes90DaysLate\"] < 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149730 entries, 0 to 149999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   SeriousDlqin2yrs                      149730 non-null  int64  \n",
      " 1   RevolvingUtilizationOfUnsecuredLines  149730 non-null  float64\n",
      " 2   age                                   149730 non-null  int64  \n",
      " 3   NumberOfTime30-59DaysPastDueNotWorse  149730 non-null  int64  \n",
      " 4   DebtRatio                             149730 non-null  float64\n",
      " 5   MonthlyIncome                         149730 non-null  float64\n",
      " 6   NumberOfOpenCreditLinesAndLoans       149730 non-null  int64  \n",
      " 7   NumberOfTimes90DaysLate               149730 non-null  int64  \n",
      " 8   NumberRealEstateLoansOrLines          149730 non-null  int64  \n",
      " 9   NumberOfTime60-89DaysPastDueNotWorse  149730 non-null  int64  \n",
      " 10  NumberOfDependents                    149730 non-null  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>0.065979</td>\n",
       "      <td>0.248246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>6.057542</td>\n",
       "      <td>249.980364</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002943</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>0.153488</td>\n",
       "      <td>0.555592</td>\n",
       "      <td>0.977992</td>\n",
       "      <td>1.093343</td>\n",
       "      <td>50708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>52.327984</td>\n",
       "      <td>14.754371</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>0.245789</td>\n",
       "      <td>0.697779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>353.631316</td>\n",
       "      <td>2039.601344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032376</td>\n",
       "      <td>0.175994</td>\n",
       "      <td>0.367119</td>\n",
       "      <td>0.870023</td>\n",
       "      <td>1270.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>329664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>5421.336609</td>\n",
       "      <td>13257.923564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>4416.000000</td>\n",
       "      <td>7408.750000</td>\n",
       "      <td>10788.100000</td>\n",
       "      <td>23235.500000</td>\n",
       "      <td>3008750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>8.467949</td>\n",
       "      <td>5.138107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>0.485529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>1.020063</td>\n",
       "      <td>1.129961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>0.064823</td>\n",
       "      <td>0.330074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>149730.0</td>\n",
       "      <td>0.738142</td>\n",
       "      <td>1.107373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count         mean           std  \\\n",
       "SeriousDlqin2yrs                      149730.0     0.065979      0.248246   \n",
       "RevolvingUtilizationOfUnsecuredLines  149730.0     6.057542    249.980364   \n",
       "age                                   149730.0    52.327984     14.754371   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  149730.0     0.245789      0.697779   \n",
       "DebtRatio                             149730.0   353.631316   2039.601344   \n",
       "MonthlyIncome                         149730.0  5421.336609  13257.923564   \n",
       "NumberOfOpenCreditLinesAndLoans       149730.0     8.467949      5.138107   \n",
       "NumberOfTimes90DaysLate               149730.0     0.090456      0.485529   \n",
       "NumberRealEstateLoansOrLines          149730.0     1.020063      1.129961   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  149730.0     0.064823      0.330074   \n",
       "NumberOfDependents                    149730.0     0.738142      1.107373   \n",
       "\n",
       "                                       min    1%        10%          25%  \\\n",
       "SeriousDlqin2yrs                       0.0   0.0   0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines   0.0   0.0   0.002943     0.029771   \n",
       "age                                   21.0  24.0  33.000000    41.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse   0.0   0.0   0.000000     0.000000   \n",
       "DebtRatio                              0.0   0.0   0.032376     0.175994   \n",
       "MonthlyIncome                          0.0   0.0   0.170000  1800.000000   \n",
       "NumberOfOpenCreditLinesAndLoans        0.0   0.0   3.000000     5.000000   \n",
       "NumberOfTimes90DaysLate                0.0   0.0   0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines           0.0   0.0   0.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse   0.0   0.0   0.000000     0.000000   \n",
       "NumberOfDependents                     0.0   0.0   0.000000     0.000000   \n",
       "\n",
       "                                              50%          75%           90%  \\\n",
       "SeriousDlqin2yrs                         0.000000     0.000000      0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.153488     0.555592      0.977992   \n",
       "age                                     52.000000    63.000000     72.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000     0.000000      1.000000   \n",
       "DebtRatio                                0.367119     0.870023   1270.000000   \n",
       "MonthlyIncome                         4416.000000  7408.750000  10788.100000   \n",
       "NumberOfOpenCreditLinesAndLoans          8.000000    11.000000     15.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000     0.000000      0.000000   \n",
       "NumberRealEstateLoansOrLines             1.000000     2.000000      2.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000     0.000000      0.000000   \n",
       "NumberOfDependents                       0.000000     1.000000      2.000000   \n",
       "\n",
       "                                               99%        max  \n",
       "SeriousDlqin2yrs                          1.000000        1.0  \n",
       "RevolvingUtilizationOfUnsecuredLines      1.093343    50708.0  \n",
       "age                                      87.000000      109.0  \n",
       "NumberOfTime30-59DaysPastDueNotWorse      3.000000       13.0  \n",
       "DebtRatio                              4983.000000   329664.0  \n",
       "MonthlyIncome                         23235.500000  3008750.0  \n",
       "NumberOfOpenCreditLinesAndLoans          24.000000       58.0  \n",
       "NumberOfTimes90DaysLate                   2.000000       17.0  \n",
       "NumberRealEstateLoansOrLines              4.000000       54.0  \n",
       "NumberOfTime60-89DaysPastDueNotWorse      2.000000       11.0  \n",
       "NumberOfDependents                        4.000000       20.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe([0.01,0.1,0.25,0.5,0.75,0.9,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149730 entries, 0 to 149729\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   SeriousDlqin2yrs                      149730 non-null  int64  \n",
      " 1   RevolvingUtilizationOfUnsecuredLines  149730 non-null  float64\n",
      " 2   age                                   149730 non-null  int64  \n",
      " 3   NumberOfTime30-59DaysPastDueNotWorse  149730 non-null  int64  \n",
      " 4   DebtRatio                             149730 non-null  float64\n",
      " 5   MonthlyIncome                         149730 non-null  float64\n",
      " 6   NumberOfOpenCreditLinesAndLoans       149730 non-null  int64  \n",
      " 7   NumberOfTimes90DaysLate               149730 non-null  int64  \n",
      " 8   NumberRealEstateLoansOrLines          149730 non-null  int64  \n",
      " 9   NumberOfTime60-89DaysPastDueNotWorse  149730 non-null  int64  \n",
      " 10  NumberOfDependents                    149730 non-null  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "data.index = range(data.shape[0])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
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       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据存在偏态，因为要服务业务，如果对数据作标准化，归一化业务人员没有办法知道具体的特征含义，所以先用这样的数据训练。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理样本不均衡问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:, 1:]\n",
    "y = data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    139851\n",
       "1      9879\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本个数:149730; 1占6.60%; 0占93.40%\n"
     ]
    }
   ],
   "source": [
    "n_sample = x.shape[0]\n",
    "y.value_counts()\n",
    "n_1_sample = y.value_counts()[1]\n",
    "n_0_sample = y.value_counts()[0]\n",
    "print('样本个数:{}; 1占{:.2%}; 0占{:.2%}'.format(n_sample, n_1_sample/n_sample, n_0_sample/n_sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 专门处理不均衡数据的库\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state = 42)\n",
    "x, y = sm.fit_sample(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(279702, 10)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本个数:279702; 1占50.00%; 0占50.00%\n"
     ]
    }
   ],
   "source": [
    "n_sample_ = x.shape[0]\n",
    "pd.Series(y).value_counts()\n",
    "n_1_sample = pd.Series(y).value_counts()[1]\n",
    "n_0_sample = pd.Series(y).value_counts()[0]\n",
    "print('样本个数:{}; 1占{:.2%}; 0占{:.2%}'.format(n_sample_,n_1_sample/n_sample_,n_0_sample/n_sample_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(x)\n",
    "y = pd.DataFrame(y)\n",
    "\n",
    "X_train, X_vali, Y_train, Y_vali = train_test_split(x,y,test_size=0.3,random_state=420)\n",
    "\n",
    "model_data = pd.concat([Y_train, X_train], axis=1)\n",
    "model_data.index = range(model_data.shape[0])\n",
    "\n",
    "vali_data = pd.concat([Y_vali, X_vali], axis=1)\n",
    "vali_data.index = range(vali_data.shape[0])\n",
    "\n",
    "model_data.columns = data.columns\n",
    "vali_data.columns = data.columns\n",
    "\n",
    "model_data.to_csv(r\"../data/logisitcRegression/model_data.csv\")\n",
    "vali_data.to_csv(r\"../data/logisitcRegression/vali_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2853.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.285915</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279606</td>\n",
       "      <td>3250.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4374.607738</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.920360</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.213572</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.117644</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>44916.677238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.271712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 0                              1.000000   44   \n",
       "1                 0                              0.285915   39   \n",
       "2                 0                              0.000000   71   \n",
       "3                 0                              0.920360   36   \n",
       "4                 1                              1.117644   50   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                     1  2853.000000       0.070000   \n",
       "1                                     0     0.279606    3250.000000   \n",
       "2                                     0     0.000000    4374.607738   \n",
       "3                                     1     0.213572    3300.000000   \n",
       "4                                     0     0.000331   44916.677238   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                8                        0   \n",
       "1                                5                        0   \n",
       "2                                2                        0   \n",
       "3                               10                        0   \n",
       "4                                0                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             1                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             0                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0            0.000000  \n",
       "1            0.000000  \n",
       "2            0.000000  \n",
       "3            0.000000  \n",
       "4            0.271712  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分箱\n",
    "离散化连续变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>qcut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2853.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(43.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.285915</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279606</td>\n",
       "      <td>3250.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.0, 39.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4374.607738</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(68.0, 74.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.920360</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.213572</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(34.0, 36.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.117644</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>44916.677238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.271712</td>\n",
       "      <td>(48.0, 50.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 0                              1.000000   44   \n",
       "1                 0                              0.285915   39   \n",
       "2                 0                              0.000000   71   \n",
       "3                 0                              0.920360   36   \n",
       "4                 1                              1.117644   50   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                     1  2853.000000       0.070000   \n",
       "1                                     0     0.279606    3250.000000   \n",
       "2                                     0     0.000000    4374.607738   \n",
       "3                                     1     0.213572    3300.000000   \n",
       "4                                     0     0.000331   44916.677238   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                8                        0   \n",
       "1                                5                        0   \n",
       "2                                2                        0   \n",
       "3                               10                        0   \n",
       "4                                0                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             1                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             0                                     0   \n",
       "\n",
       "   NumberOfDependents          qcut  \n",
       "0            0.000000  (43.0, 45.0]  \n",
       "1            0.000000  (36.0, 39.0]  \n",
       "2            0.000000  (68.0, 74.0]  \n",
       "3            0.000000  (34.0, 36.0]  \n",
       "4            0.271712  (48.0, 50.0]  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照等频对需要分箱的列进行分箱,这里对age进行分箱\n",
    "#分成20箱\n",
    "\n",
    "model_data[\"qcut\"], updown = pd.qcut(model_data[\"age\"], retbins=True, q=20)\n",
    "\"\"\"\n",
    "pd.qcut,基于分位数的分箱函数,本质是将连续型变量离散化\n",
    "只能够处理一维数据。返回箱子的上限和下限\n",
    "参数q:要分箱的个数\n",
    "参数retbins=True来要求同时返回结构为索引为样本索引,元素为分到的箱子的Series\n",
    "现在返回两个值:每个样本属于哪个箱子,以及所有箱子的上限和下限\n",
    "\"\"\"\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 21.,  28.,  31.,  34.,  36.,  39.,  41.,  43.,  45.,  47.,  48.,\n",
       "        50.,  52.,  54.,  56.,  58.,  61.,  64.,  68.,  74., 109.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计每个分箱中0和1的数量\n",
    "# 这里使用了数据透视表的功能groupby\n",
    "coount_y0 = model_data[model_data[\"SeriousDlqin2yrs\"] == 0].groupby(by=\"qcut\").count()[\"SeriousDlqin2yrs\"]\n",
    "coount_y1 = model_data[model_data[\"SeriousDlqin2yrs\"] == 1].groupby(by=\"qcut\").count()[\"SeriousDlqin2yrs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 28.0, 4326, 7528),\n",
       " (28.0, 31.0, 3557, 6056),\n",
       " (31.0, 34.0, 3984, 6801),\n",
       " (34.0, 36.0, 2899, 4608),\n",
       " (36.0, 39.0, 5169, 7608),\n",
       " (39.0, 41.0, 3935, 5939),\n",
       " (41.0, 43.0, 3975, 5651),\n",
       " (43.0, 45.0, 4380, 6002),\n",
       " (45.0, 47.0, 4767, 6417),\n",
       " (47.0, 48.0, 2481, 3110),\n",
       " (48.0, 50.0, 4896, 6064),\n",
       " (50.0, 52.0, 4759, 5929),\n",
       " (52.0, 54.0, 4679, 4906),\n",
       " (54.0, 56.0, 4549, 4173),\n",
       " (56.0, 58.0, 4507, 3508),\n",
       " (58.0, 61.0, 6764, 4777),\n",
       " (61.0, 64.0, 6982, 3127),\n",
       " (64.0, 68.0, 6679, 2385),\n",
       " (68.0, 74.0, 6863, 1897),\n",
       " (74.0, 109.0, 7797, 1357)]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#num_bins值分别为每个区间的上界,下界,0出现的次数,1出现的次数\n",
    "num_bins = [*zip(updown,updown[1:],coount_y0,coount_y1)]\n",
    "#注意zip会按照最短列来进行结合\n",
    "num_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count_0</th>\n",
       "      <th>count_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4326</td>\n",
       "      <td>7528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3557</td>\n",
       "      <td>6056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3984</td>\n",
       "      <td>6801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2899</td>\n",
       "      <td>4608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5169</td>\n",
       "      <td>7608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3935</td>\n",
       "      <td>5939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3975</td>\n",
       "      <td>5651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4380</td>\n",
       "      <td>6002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4767</td>\n",
       "      <td>6417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2481</td>\n",
       "      <td>3110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4896</td>\n",
       "      <td>6064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4759</td>\n",
       "      <td>5929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>52.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4679</td>\n",
       "      <td>4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>54.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4549</td>\n",
       "      <td>4173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>56.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4507</td>\n",
       "      <td>3508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>58.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>6764</td>\n",
       "      <td>4777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>61.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6982</td>\n",
       "      <td>3127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>64.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6679</td>\n",
       "      <td>2385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6863</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>74.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>7797</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     min    max  count_0  count_1\n",
       "0   21.0   28.0     4326     7528\n",
       "1   28.0   31.0     3557     6056\n",
       "2   31.0   34.0     3984     6801\n",
       "3   34.0   36.0     2899     4608\n",
       "4   36.0   39.0     5169     7608\n",
       "5   39.0   41.0     3935     5939\n",
       "6   41.0   43.0     3975     5651\n",
       "7   43.0   45.0     4380     6002\n",
       "8   45.0   47.0     4767     6417\n",
       "9   47.0   48.0     2481     3110\n",
       "10  48.0   50.0     4896     6064\n",
       "11  50.0   52.0     4759     5929\n",
       "12  52.0   54.0     4679     4906\n",
       "13  54.0   56.0     4549     4173\n",
       "14  56.0   58.0     4507     3508\n",
       "15  58.0   61.0     6764     4777\n",
       "16  61.0   64.0     6982     3127\n",
       "17  64.0   68.0     6679     2385\n",
       "18  68.0   74.0     6863     1897\n",
       "19  74.0  109.0     7797     1357"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\"min\", \"max\", \"count_0\", \"count_1\"]\n",
    "df = pd.DataFrame(num_bins, columns=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义计算WOE和IV的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算WOE和BAD RATE\n",
    "#BAD RATE与bad%不是一个东西\n",
    "#BAD RATE是一个箱中,坏的样本所占的比例 (bad/total)\n",
    "#而bad%是一个箱中的坏样本占整个特征中的坏样本的比例\n",
    "def get_woe(num_bins):\n",
    "    # 通过 num_bins 数据计算 woe\n",
    "    columns = [\"min\",\"max\",\"count_0\",\"count_1\"]\n",
    "    df = pd.DataFrame(num_bins,columns=columns)\n",
    "    df[\"total\"] = df.count_0 + df.count_1\n",
    "    df[\"percentage\"] = df.total / df.total.sum()\n",
    "    df[\"bad_rate\"] = df.count_1 / df.total\n",
    "    df[\"good%\"] = df.count_0/df.count_0.sum()\n",
    "    df[\"bad%\"] = df.count_1/df.count_1.sum()\n",
    "    df[\"woe\"] = np.log(df[\"good%\"] / df[\"bad%\"])\n",
    "    return df\n",
    "\n",
    "#计算IV值\n",
    "def get_iv(df):\n",
    "    rate = df[\"good%\"] - df[\"bad%\"]\n",
    "    iv = np.sum(rate * df.woe)\n",
    "    return iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卡方检验,合并箱体,画出IV曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins_ = num_bins.copy()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "\n",
    "IV = []\n",
    "axisx = []\n",
    "\n",
    "# 2控制最终的合并箱体个数\n",
    "while len(num_bins_) > 2:\n",
    "    pvs = []\n",
    "    for i in range(len(num_bins_)-1):\n",
    "        x1 = num_bins_[i][2:]\n",
    "        # 获取 num_bins_两两之间的卡方检验的置信度(或卡方值)\n",
    "        x2 = num_bins_[i+1][2:]\n",
    "        # 0 返回 chi2 值,1 返回 p 值。计算卡方值\n",
    "        pv = scipy.stats.chi2_contingency([x1,x2])[1]\n",
    "        # chi2 = scipy.stats.chi2_contingency([x1,x2])[0]\n",
    "        pvs.append(pv)\n",
    "    # 通过 p 值进行处理。合并 p 值最大的两组\n",
    "    i = pvs.index(max(pvs))\n",
    "    num_bins_[i:i+2] = [(\n",
    "                        num_bins_[i][0],\n",
    "                        num_bins_[i+1][1],\n",
    "                        num_bins_[i][2]+num_bins_[i+1][2],\n",
    "                        num_bins_[i][3]+num_bins_[i+1][3])]\n",
    "    bins_df = get_woe(num_bins_)\n",
    "    axisx.append(len(num_bins_))\n",
    "    IV.append(get_iv(bins_df))\n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(axisx,IV)\n",
    "plt.xticks(axisx)\n",
    "plt.xlabel(\"number of box\")\n",
    "plt.ylabel(\"IV\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用最佳分箱个数分箱，并验证分箱结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bin(num_bins_,n):\n",
    "    while len(num_bins_) > n:\n",
    "        pvs = []\n",
    "        for i in range(len(num_bins_)-1):\n",
    "            x1 = num_bins_[i][2:]\n",
    "            x2 = num_bins_[i+1][2:]\n",
    "            pv = scipy.stats.chi2_contingency([x1,x2])[1]\n",
    "            # chi2 = scipy.stats.chi2_contingency([x1,x2])[0]\n",
    "            pvs.append(pv)\n",
    "        i = pvs.index(max(pvs))\n",
    "        num_bins_[i:i+2] = [(\n",
    "                            num_bins_[i][0],\n",
    "                            num_bins_[i+1][1],\n",
    "                            num_bins_[i][2]+num_bins_[i+1][2],\n",
    "                            num_bins_[i][3]+num_bins_[i+1][3])]\n",
    "    return num_bins_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afterbins = get_bin(num_bins,7)\n",
    "len(afterbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 36.0, 14766, 24993),\n",
       " (36.0, 52.0, 34362, 46720),\n",
       " (52.0, 56.0, 9228, 9079),\n",
       " (56.0, 61.0, 11271, 8285),\n",
       " (61.0, 68.0, 13661, 5512),\n",
       " (68.0, 74.0, 6863, 1897),\n",
       " (74.0, 109.0, 7797, 1357)]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afterbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count_0</th>\n",
       "      <th>count_1</th>\n",
       "      <th>total</th>\n",
       "      <th>percentage</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good%</th>\n",
       "      <th>bad%</th>\n",
       "      <th>woe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>14766</td>\n",
       "      <td>24993</td>\n",
       "      <td>39759</td>\n",
       "      <td>0.203069</td>\n",
       "      <td>0.628612</td>\n",
       "      <td>0.150753</td>\n",
       "      <td>0.255440</td>\n",
       "      <td>-0.527341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34362</td>\n",
       "      <td>46720</td>\n",
       "      <td>81082</td>\n",
       "      <td>0.414125</td>\n",
       "      <td>0.576207</td>\n",
       "      <td>0.350819</td>\n",
       "      <td>0.477500</td>\n",
       "      <td>-0.308294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>9228</td>\n",
       "      <td>9079</td>\n",
       "      <td>18307</td>\n",
       "      <td>0.093503</td>\n",
       "      <td>0.495931</td>\n",
       "      <td>0.094213</td>\n",
       "      <td>0.092792</td>\n",
       "      <td>0.015206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>11271</td>\n",
       "      <td>8285</td>\n",
       "      <td>19556</td>\n",
       "      <td>0.099882</td>\n",
       "      <td>0.423655</td>\n",
       "      <td>0.115071</td>\n",
       "      <td>0.084676</td>\n",
       "      <td>0.306714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>13661</td>\n",
       "      <td>5512</td>\n",
       "      <td>19173</td>\n",
       "      <td>0.097926</td>\n",
       "      <td>0.287488</td>\n",
       "      <td>0.139472</td>\n",
       "      <td>0.056335</td>\n",
       "      <td>0.906545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6863</td>\n",
       "      <td>1897</td>\n",
       "      <td>8760</td>\n",
       "      <td>0.044742</td>\n",
       "      <td>0.216553</td>\n",
       "      <td>0.070068</td>\n",
       "      <td>0.019388</td>\n",
       "      <td>1.284798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>7797</td>\n",
       "      <td>1357</td>\n",
       "      <td>9154</td>\n",
       "      <td>0.046754</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.079603</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>1.747390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min    max  count_0  count_1  total  percentage  bad_rate     good%  \\\n",
       "0  21.0   36.0    14766    24993  39759    0.203069  0.628612  0.150753   \n",
       "1  36.0   52.0    34362    46720  81082    0.414125  0.576207  0.350819   \n",
       "2  52.0   56.0     9228     9079  18307    0.093503  0.495931  0.094213   \n",
       "3  56.0   61.0    11271     8285  19556    0.099882  0.423655  0.115071   \n",
       "4  61.0   68.0    13661     5512  19173    0.097926  0.287488  0.139472   \n",
       "5  68.0   74.0     6863     1897   8760    0.044742  0.216553  0.070068   \n",
       "6  74.0  109.0     7797     1357   9154    0.046754  0.148241  0.079603   \n",
       "\n",
       "       bad%       woe  \n",
       "0  0.255440 -0.527341  \n",
       "1  0.477500 -0.308294  \n",
       "2  0.092792  0.015206  \n",
       "3  0.084676  0.306714  \n",
       "4  0.056335  0.906545  \n",
       "5  0.019388  1.284798  \n",
       "6  0.013869  1.747390  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bins_dft = get_woe(num_bins)\n",
    "# bins_dft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将选取最佳分箱个数的过程封装为函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphforbestbin(DF, X, Y, n=5,q=20,graph=True):\n",
    "    \"\"\"\n",
    "    自动最优分箱函数,基于卡方检验的分箱\n",
    "    参数:\n",
    "    DF: 需要输入的数据\n",
    "    X: 需要分箱的列名\n",
    "    Y: 分箱数据对应的标签 Y 列名\n",
    "    n: 保留分箱个数\n",
    "    q: 初始分箱的个数\n",
    "    graph: 是否要画出IV图像\n",
    "    区间为前开后闭 (]\n",
    "    \"\"\"\n",
    "    DF = DF[[X,Y]].copy()\n",
    "    \n",
    "    DF[\"qcut\"],bins = pd.qcut(DF[X], retbins=True, q=q,duplicates=\"drop\")\n",
    "    coount_y0 = DF.loc[DF[Y]==0].groupby(by=\"qcut\").count()[Y]\n",
    "    coount_y1 = DF.loc[DF[Y]==1].groupby(by=\"qcut\").count()[Y]\n",
    "    num_bins = [*zip(bins,bins[1:],coount_y0,coount_y1)]\n",
    "    \n",
    "    for i in range(q):\n",
    "        if 0 in num_bins[0][2:]:\n",
    "            num_bins[0:2] = [(\n",
    "                            num_bins[0][0],\n",
    "                            num_bins[1][1],\n",
    "                            num_bins[0][2]+num_bins[1][2],\n",
    "                            num_bins[0][3]+num_bins[1][3])]\n",
    "            continue\n",
    "        for i in range(len(num_bins)):\n",
    "            if 0 in num_bins[i][2:]:\n",
    "                num_bins[i-1:i+1] = [(\n",
    "                                    num_bins[i-1][0],\n",
    "                                    num_bins[i][1],\n",
    "                                    num_bins[i-1][2]+num_bins[i][2],\n",
    "                                    num_bins[i-1][3]+num_bins[i][3])]\n",
    "                break\n",
    "        else:\n",
    "            break\n",
    "        \n",
    "    def get_woe(num_bins):\n",
    "        columns = [\"min\",\"max\",\"count_0\",\"count_1\"]\n",
    "        df = pd.DataFrame(num_bins,columns=columns)\n",
    "        df[\"total\"] = df.count_0 + df.count_1\n",
    "        df[\"percentage\"] = df.total / df.total.sum()\n",
    "        df[\"bad_rate\"] = df.count_1 / df.total\n",
    "        df[\"good%\"] = df.count_0/df.count_0.sum()\n",
    "        df[\"bad%\"] = df.count_1/df.count_1.sum()\n",
    "        df[\"woe\"] = np.log(df[\"good%\"] / df[\"bad%\"])\n",
    "        return df\n",
    "    \n",
    "    def get_iv(df):\n",
    "        rate = df[\"good%\"] - df[\"bad%\"]\n",
    "        iv = np.sum(rate * df.woe)\n",
    "        return iv\n",
    "    \n",
    "    IV = []\n",
    "    axisx = []\n",
    "    \n",
    "    while len(num_bins) > n:\n",
    "        pvs = []\n",
    "        for i in range(len(num_bins)-1):\n",
    "            x1 = num_bins[i][2:]\n",
    "            x2 = num_bins[i+1][2:]\n",
    "            pv = scipy.stats.chi2_contingency([x1,x2])[1]\n",
    "            pvs.append(pv)\n",
    "            \n",
    "        i = pvs.index(max(pvs))\n",
    "        num_bins[i:i+2] = [(\n",
    "                            num_bins[i][0],\n",
    "                            num_bins[i+1][1],\n",
    "                            num_bins[i][2]+num_bins[i+1][2],\n",
    "                            num_bins[i][3]+num_bins[i+1][3])]\n",
    "        global bins_df\n",
    "        bins_df = pd.DataFrame(get_woe(num_bins))\n",
    "        axisx.append(len(num_bins))\n",
    "        IV.append(get_iv(bins_df))\n",
    "        \n",
    "    if graph:\n",
    "        plt.figure()\n",
    "        plt.plot(axisx,IV)\n",
    "        plt.xticks(axisx)\n",
    "        plt.xlabel(\"number of box\")\n",
    "        plt.ylabel(\"IV\")\n",
    "        plt.show()\n",
    "        \n",
    "    return bins_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = model_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime30-59DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebtRatio\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfOpenCreditLinesAndLoans\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTimes90DaysLate\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberRealEstateLoansOrLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime60-89DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfDependents\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in test_data.columns[1:-1]:\n",
    "    print(i)\n",
    "    graphforbestbin(test_data,i,\"SeriousDlqin2yrs\",n=2,q=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime30-59DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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2Nu12Am98pceTJLVjlKGSAWU1VMfefZJlwCLgTOCUJB9o43hJrk7STdKdmJgYZjqSpCGNMlR2AGf37Z/Fy5ewpnMpsL2qJqpqL3A78Pam7tkkZwA0f3fN5HhVtbqqOlXVGRsbG3oxkqTpjTJUNgDjSRYlOYHeTfT1Q/Z9EnhbknlJAlwCPNrUrQeuaravAv6qr3x5khOTLALGgftbWIckaUhzRzVwVU0mWQncQe/prTVVtTnJiqZ+VZLTgS4wH9if5DpgcVXdl+QrwAPAJPAgsLoZ+g+A25J8mF74/GYz3uYktwGPNH2uqap9o1qfJOlgqRrqNserUqfTqW63O9vTkKTjSpKNVdUZVOc36iVJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVQkSa0ZaagkWZpkS5KtSW4aUH9eknuS7ElyY1/5LyZ5qO/zQvP79ST5Ul/5E0keasoXJvlJX92qUa5NknSwuaMaOMkc4FbgncAOYEOS9VX1SF+z3cC1wOX9fatqC3BB3zhPA19r6t7fd4xPAc/3dX28qi5ofzWSpGGM8kxlCbC1qrZV1UvAOmBZf4Oq2lVVG4C9hxnnEnph8YP+wiQBfgtY2+60JUlHapShsgB4qm9/R1M2U8sZHBwXAc9W1WN9ZYuSPJjk7iQXDRosydVJukm6ExMTRzAdSdKhjDJUMqCsZjRAcgLwXuDLA6qv5KfDZidwTlVdCNwAfDHJ/IMmULW6qjpV1RkbG5vJdCRJ0xhlqOwAzu7bPwt4ZoZjXAY8UFXP9hcmmQu8D/jSgbKq2lNVzzXbG4HHgTcdwbwlSUdolKGyARhPsqg541gOrJ/hGFPPRg64FPheVe04UJBkrLmpT5JzgXFg2xHNXJJ0REb29FdVTSZZCdwBzAHWVNXmJCua+lVJTge6wHxgf/PY8OKqeiHJPHpPjn10wPCD7rNcDHwyySSwD1hRVbtHsjhJ0kCpmtFtjleVTqdT3W53tqchSceVJBurqjOozm/US5JaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWjPSUEmyNMmWJFuT3DSg/rwk9yTZk+TGvvJfTPJQ3+eF5vfrSfL7SZ7uq3t3X7+bm2NtSfKuUa5NknSwuaMaOMkc4FbgncAOYEOS9VX1SF+z3cC1wOX9fatqC3BB3zhPA1/ra/LpqrplyvEWA8uBNwNnAn+b5E1Vta/VhUmSDmmUZypLgK1Vta2qXgLWAcv6G1TVrqraAOw9zDiXAI9X1Q+mOd4yYF1V7amq7cDWZg6SpKNklKGyAHiqb39HUzZTy4G1U8pWJnk4yZokp87keEmuTtJN0p2YmDiC6UiSDmWUoZIBZTWjAZITgPcCX+4r/lPg5+ldHtsJfGomx6uq1VXVqarO2NjYTKYjSZrGKENlB3B23/5ZwDMzHOMy4IGqevZAQVU9W1X7qmo/8DlevsTVxvEkSa/AKENlAzCeZFFzxrEcWD/DMa5kyqWvJGf07V4BbGq21wPLk5yYZBEwDtx/RDOXJB2RkT39VVWTSVYCdwBzgDVVtTnJiqZ+VZLTgS4wH9jfPDa8uKpeSDKP3pNjH50y9B8luYDepa0nDtQ3Y98GPAJMAtf45JckHV2pmtFtjleVTqdT3W53tqchSceVJBurqjOozm/US5JaY6hIklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklrzmv7yY5IJYLpX6kuz5TTgh7M9CWmAn6uqgW/kfU2HinQsS9I91LeWpWOVl78kSa0xVCRJrTFUpGPX6tmegDRT3lORJLXGMxVJUmsMFUlSawwV6RiS5Owk30ryaJLNST4223OSZsJ7KtIxJMkZwBlV9UCSnwE2ApdX1SOzPDVpKJ6pSMeQqtpZVQ802z8GHgUWzO6spOEZKtIxKslC4ELgvtmdiTQ8Q0U6BiX5N8BXgeuq6oXZno80LENFOsYk+df0AuULVXX7bM9Hmglv1EvHkCQBPg/srqrrZns+0kwZKtIxJMk7gP8LfBfY3xT/XlX99ezNShqeoSJJao33VCRJrTFUJEmtMVQkSa0xVCRJrTFUJEmtMVSkEUvy90k6R+E41zZvN/7ClPIPJvnMqI8vAcyd7QlIOrQkc6tqcsjm/xm4rKq2j3JO0uF4piLRe3lj83/5n2t+x+TOJCc3df9yppHktCRPNNsfTPKXSb6eZHuSlUluSPJgknuTvL7vEB9I8p0km5IsafqfkmRNkg1Nn2V94345ydeBOwfM9YZmnE1JrmvKVgHnAuuTXD9giWcn+ZskW5L8t2nGuiHJmmb7l5u6ea/031ivDZ6pSC8bB66sqo8kuQ34DeAvpulzPr03CZ8EbAU+XlUXJvk08DvAnzTtTqmqtye5GFjT9PsE8HdV9aEkrwPuT/K3Tft/B7ylqnb3HyzJW4H/BPwaEOC+JHdX1YokS4F/X1U/HDDPJc0xXwQ2JPk/QA0aq5nz3ye5opnjR6vqxen+8SQwVKR+26vqoWZ7I7BwiD7fan735MdJnge+3pR/F3hLX7u1AFX17STzmxD5deC9SW5s2pwEnNNs3zU1UBrvAL5WVf8MkOR24CLgwWnmeVdVPdfX5x30QuWgsarqwSQfBB4G/mdV/cMQ/w4SYKhI/fb0be8DTm62J3n5UvFJh+mzv29/Pz/939fU9yEVvbOD36iqLf0VSX4N+OdDzDGHmvw0DnX8QxkH/gk48wiPp9co76lI03sCeGuz/R+OcIz3w7+8MPL5qnoeuAP4L82biUly4RDjfBu4PMm8JKcAV9B7AeV03pnk9c19osuBfzjUWEn+LfA/gIuBNyQ50jXrNcgzFWl6twC3JfmPwN8d4Rg/SvIdYD7woabsv9O7f/FwEyxPAO853CDNb9f/OXB/U/S/qmq6S18A/w/438AvAF+sqi7AoLGam/SfrarvJ/kw8K0k366qXcMtVa9lvqVYktQaL39JklpjqEiSWmOoSJJaY6hIklpjqEiSWmOoSJJaY6hIklrz/wFLVRk45EOOtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebtRatio\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfOpenCreditLinesAndLoans\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTimes90DaysLate\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberRealEstateLoansOrLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime60-89DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfDependents\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in test_data.columns[1:-1]:\n",
    "    print(i)\n",
    "    graphforbestbin(model_data,i,\"SeriousDlqin2yrs\",n=2,q=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有的特征无法分箱，于是需要手动分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_col_bins = {\"RevolvingUtilizationOfUnsecuredLines\":6,\n",
    "                \"age\":5,\n",
    "                \"DebtRatio\":4,\n",
    "                \"MonthlyIncome\":3,\n",
    "                \"NumberOfOpenCreditLinesAndLoans\":5}\n",
    "#不能使用自动分箱的变量\n",
    "hand_bins = {\"NumberOfTime30-59DaysPastDueNotWorse\":[0,1,2,13]\n",
    "            ,\"NumberOfTimes90DaysLate\":[0,1,2,17]\n",
    "            ,\"NumberRealEstateLoansOrLines\":[0,1,2,4,54]\n",
    "            ,\"NumberOfTime60-89DaysPastDueNotWorse\":[0,1,2,8]\n",
    "            ,\"NumberOfDependents\":[0,1,2,3]}\n",
    "\n",
    "#保证区间覆盖使用 np.inf替换最大值,用-np.inf替换最小值\n",
    "hand_bins = {k:[-np.inf,*v[:-1],np.inf] for k,v in hand_bins.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NumberOfTime30-59DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfTimes90DaysLate': [-inf, 0, 1, 2, inf],\n",
       " 'NumberRealEstateLoansOrLines': [-inf, 0, 1, 2, 4, inf],\n",
       " 'NumberOfTime60-89DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfDependents': [-inf, 0, 1, 2, inf]}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hand_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RevolvingUtilizationOfUnsecuredLines': [-inf,\n",
       "  0.099036335,\n",
       "  0.297094601,\n",
       "  0.4649045351851297,\n",
       "  0.9845728010575414,\n",
       "  0.9999998999999999,\n",
       "  inf],\n",
       " 'age': [-inf, 36.0, 52.0, 61.0, 68.0, inf],\n",
       " 'DebtRatio': [-inf,\n",
       "  0.015766330351328087,\n",
       "  0.503441053,\n",
       "  1.4534816458114537,\n",
       "  inf],\n",
       " 'MonthlyIncome': [-inf, 0.39, 7716.0, inf],\n",
       " 'NumberOfOpenCreditLinesAndLoans': [-inf, 1.0, 3.0, 5.0, 17.0, inf],\n",
       " 'NumberOfTime30-59DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfTimes90DaysLate': [-inf, 0, 1, 2, inf],\n",
       " 'NumberRealEstateLoansOrLines': [-inf, 0, 1, 2, 4, inf],\n",
       " 'NumberOfTime60-89DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfDependents': [-inf, 0, 1, 2, inf]}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_of_col = {}\n",
    "# 生成自动分箱的分箱区间和分箱后的 IV 值\n",
    "for col in auto_col_bins:\n",
    "    bins_df = graphforbestbin(model_data,col\n",
    "                                ,\"SeriousDlqin2yrs\"\n",
    "                                ,n=auto_col_bins[col]\n",
    "                                #使用字典的性质来取出每个特征所对应的箱的数量\n",
    "                                ,q=20\n",
    "                                ,graph=False)\n",
    "    bins_list = sorted(set(bins_df[\"min\"]).union(bins_df[\"max\"]))\n",
    "    #保证区间覆盖使用 np.inf 替换最大值 -np.inf 替换最小值\n",
    "    bins_list[0],bins_list[-1] = -np.inf,np.inf\n",
    "    bins_of_col[col] = bins_list\n",
    "#合并手动分箱数据\n",
    "bins_of_col.update(hand_bins)\n",
    "bins_of_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得到所有特征的分箱结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算各箱的WOE并映射到数据中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RevolvingUtilizationOfUnsecuredLines': cut\n",
       " (-inf, 0.099]     2.214860\n",
       " (0.099, 0.297]    0.648362\n",
       " (0.297, 0.465]   -0.122884\n",
       " (0.465, 0.985]   -1.075355\n",
       " (0.985, 1.0]     -0.441884\n",
       " (1.0, inf]       -2.075822\n",
       " dtype: float64,\n",
       " 'age': cut\n",
       " (-inf, 36.0]   -0.527341\n",
       " (36.0, 52.0]   -0.308294\n",
       " (52.0, 61.0]    0.164904\n",
       " (61.0, 68.0]    0.906545\n",
       " (68.0, inf]     1.504165\n",
       " dtype: float64,\n",
       " 'DebtRatio': cut\n",
       " (-inf, 0.0158]     1.514030\n",
       " (0.0158, 0.503]   -0.004063\n",
       " (0.503, 1.453]    -0.482179\n",
       " (1.453, inf]       0.165099\n",
       " dtype: float64,\n",
       " 'MonthlyIncome': cut\n",
       " (-inf, 0.39]      1.051689\n",
       " (0.39, 7716.0]   -0.240271\n",
       " (7716.0, inf]     0.355063\n",
       " dtype: float64,\n",
       " 'NumberOfOpenCreditLinesAndLoans': cut\n",
       " (-inf, 1.0]   -0.815295\n",
       " (1.0, 3.0]    -0.341034\n",
       " (3.0, 5.0]    -0.041791\n",
       " (5.0, 17.0]    0.126457\n",
       " (17.0, inf]    0.427228\n",
       " dtype: float64,\n",
       " 'NumberOfTime30-59DaysPastDueNotWorse': cut\n",
       " (-inf, 0.0]    0.352206\n",
       " (0.0, 1.0]    -0.882564\n",
       " (1.0, 2.0]    -1.357501\n",
       " (2.0, inf]    -1.515748\n",
       " dtype: float64,\n",
       " 'NumberOfTimes90DaysLate': cut\n",
       " (-inf, 0.0]    0.234244\n",
       " (0.0, 1.0]    -1.748151\n",
       " (1.0, 2.0]    -2.244039\n",
       " (2.0, inf]    -2.374249\n",
       " dtype: float64,\n",
       " 'NumberRealEstateLoansOrLines': cut\n",
       " (-inf, 0.0]   -0.388086\n",
       " (0.0, 1.0]     0.194917\n",
       " (1.0, 2.0]     0.619746\n",
       " (2.0, 4.0]     0.377023\n",
       " (4.0, inf]    -0.419904\n",
       " dtype: float64,\n",
       " 'NumberOfTime60-89DaysPastDueNotWorse': cut\n",
       " (-inf, 0.0]    0.123246\n",
       " (0.0, 1.0]    -1.389716\n",
       " (1.0, 2.0]    -1.783840\n",
       " (2.0, inf]    -1.852862\n",
       " dtype: float64,\n",
       " 'NumberOfDependents': cut\n",
       " (-inf, 0.0]    0.628330\n",
       " (0.0, 1.0]    -0.588904\n",
       " (1.0, 2.0]    -0.523712\n",
       " (2.0, inf]    -0.492403\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_woe(df,col,y,bins):\n",
    "    df = df[[col,y]].copy()\n",
    "    df[\"cut\"] = pd.cut(df[col],bins)\n",
    "    bins_df = df.groupby(\"cut\")[y].value_counts().unstack()\n",
    "    woe = bins_df[\"woe\"] = np.log((bins_df[0]/bins_df[0].sum())/(bins_df[1]/bins_df[1].sum()))\n",
    "    return woe\n",
    "#将所有特征的WOE存储到字典当中\n",
    "woeall = {}\n",
    "for col in bins_of_col:\n",
    "    woeall[col] = get_woe(model_data,col,\"SeriousDlqin2yrs\",bins_of_col[col])\n",
    "woeall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.308294</td>\n",
       "      <td>-0.441884</td>\n",
       "      <td>0.165099</td>\n",
       "      <td>1.051689</td>\n",
       "      <td>0.126457</td>\n",
       "      <td>-0.882564</td>\n",
       "      <td>0.234244</td>\n",
       "      <td>0.194917</td>\n",
       "      <td>0.123246</td>\n",
       "      <td>0.628330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.308294</td>\n",
       "      <td>0.648362</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.240271</td>\n",
       "      <td>-0.041791</td>\n",
       "      <td>0.352206</td>\n",
       "      <td>0.234244</td>\n",
       "      <td>-0.388086</td>\n",
       "      <td>0.123246</td>\n",
       "      <td>0.628330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.504165</td>\n",
       "      <td>2.214860</td>\n",
       "      <td>1.514030</td>\n",
       "      <td>-0.240271</td>\n",
       "      <td>-0.341034</td>\n",
       "      <td>0.352206</td>\n",
       "      <td>0.234244</td>\n",
       "      <td>-0.388086</td>\n",
       "      <td>0.123246</td>\n",
       "      <td>0.628330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.527341</td>\n",
       "      <td>-1.075355</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.240271</td>\n",
       "      <td>0.126457</td>\n",
       "      <td>-0.882564</td>\n",
       "      <td>0.234244</td>\n",
       "      <td>-0.388086</td>\n",
       "      <td>0.123246</td>\n",
       "      <td>0.628330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.308294</td>\n",
       "      <td>-2.075822</td>\n",
       "      <td>1.514030</td>\n",
       "      <td>0.355063</td>\n",
       "      <td>-0.815295</td>\n",
       "      <td>0.352206</td>\n",
       "      <td>0.234244</td>\n",
       "      <td>-0.388086</td>\n",
       "      <td>0.123246</td>\n",
       "      <td>-0.588904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age RevolvingUtilizationOfUnsecuredLines DebtRatio MonthlyIncome  \\\n",
       "0 -0.308294                            -0.441884  0.165099      1.051689   \n",
       "1 -0.308294                             0.648362 -0.004063     -0.240271   \n",
       "2  1.504165                             2.214860  1.514030     -0.240271   \n",
       "3 -0.527341                            -1.075355 -0.004063     -0.240271   \n",
       "4 -0.308294                            -2.075822  1.514030      0.355063   \n",
       "\n",
       "  NumberOfOpenCreditLinesAndLoans NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "0                        0.126457                            -0.882564   \n",
       "1                       -0.041791                             0.352206   \n",
       "2                       -0.341034                             0.352206   \n",
       "3                        0.126457                            -0.882564   \n",
       "4                       -0.815295                             0.352206   \n",
       "\n",
       "  NumberOfTimes90DaysLate NumberRealEstateLoansOrLines  \\\n",
       "0                0.234244                     0.194917   \n",
       "1                0.234244                    -0.388086   \n",
       "2                0.234244                    -0.388086   \n",
       "3                0.234244                    -0.388086   \n",
       "4                0.234244                    -0.388086   \n",
       "\n",
       "  NumberOfTime60-89DaysPastDueNotWorse NumberOfDependents  SeriousDlqin2yrs  \n",
       "0                             0.123246           0.628330                 0  \n",
       "1                             0.123246           0.628330                 0  \n",
       "2                             0.123246           0.628330                 0  \n",
       "3                             0.123246           0.628330                 0  \n",
       "4                             0.123246          -0.588904                 1  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不希望覆盖掉原本的数据,创建一个新的DataFrame,索引和原始数据model_data一模一样\n",
    "model_woe = pd.DataFrame(index=model_data.index)\n",
    "#将原数据分箱后,按箱的结果把WOE结构用map函数映射到数据中\n",
    "# model_woe[\"age\"] = pd.cut(model_data[\"age\"],bins_of_col[\"age\"]).map(woeall[\"age\"])\n",
    "#对所有特征操作可以写成:\n",
    "for col in bins_of_col:\n",
    "    model_woe[col] = pd.cut(model_data[col],bins_of_col[col]).map(woeall[col])\n",
    "#将标签补充到数据中\n",
    "model_woe[\"SeriousDlqin2yrs\"] = model_data[\"SeriousDlqin2yrs\"]\n",
    "#这就是我们的建模数据了\n",
    "model_woe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195791 entries, 0 to 195790\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                Non-Null Count   Dtype   \n",
      "---  ------                                --------------   -----   \n",
      " 0   age                                   195791 non-null  category\n",
      " 1   RevolvingUtilizationOfUnsecuredLines  195791 non-null  category\n",
      " 2   DebtRatio                             195791 non-null  category\n",
      " 3   MonthlyIncome                         195791 non-null  category\n",
      " 4   NumberOfOpenCreditLinesAndLoans       195791 non-null  category\n",
      " 5   NumberOfTime30-59DaysPastDueNotWorse  195791 non-null  category\n",
      " 6   NumberOfTimes90DaysLate               195791 non-null  category\n",
      " 7   NumberRealEstateLoansOrLines          195791 non-null  category\n",
      " 8   NumberOfTime60-89DaysPastDueNotWorse  195791 non-null  category\n",
      " 9   NumberOfDependents                    195791 non-null  category\n",
      " 10  SeriousDlqin2yrs                      195791 non-null  int64   \n",
      "dtypes: category(10), int64(1)\n",
      "memory usage: 3.4 MB\n"
     ]
    }
   ],
   "source": [
    "model_woe.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建模与模型验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#处理测试集\n",
    "vali_woe = pd.DataFrame(index=vali_data.index)\n",
    "for col in bins_of_col:\n",
    "    vali_woe[col] = pd.cut(vali_data[col],bins_of_col[col]).map(woeall[col])\n",
    "vali_woe[\"SeriousDlqin2yrs\"] = vali_data[\"SeriousDlqin2yrs\"]\n",
    "vali_x = vali_woe.iloc[:,:-1]\n",
    "vali_y = vali_woe.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = model_woe.iloc[:,:-1]\n",
    "y = model_woe.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7711265507501996"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "\n",
    "lr = LR().fit(X,y)\n",
    "lr.score(vali_x,vali_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_1 = np.linspace(0.01,1,20)\n",
    "c_2 = np.linspace(0.01,0.2,20)\n",
    "\n",
    "score = []\n",
    "for i in c_1:\n",
    "    lr = LR(solver='liblinear',C=i).fit(X,y)\n",
    "    score.append(lr.score(vali_x,vali_y))\n",
    "plt.figure()\n",
    "plt.plot(c_2,score)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=int32)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/neu/anaconda3/envs/keras-env/lib/python3.6/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/neu/anaconda3/envs/keras-env/lib/python3.6/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/neu/anaconda3/envs/keras-env/lib/python3.6/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/neu/anaconda3/envs/keras-env/lib/python3.6/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/neu/anaconda3/envs/keras-env/lib/python3.6/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = []\n",
    "\n",
    "for i in [1,2,3,4,5,6]:\n",
    "    lr = LR(solver='liblinear',C=0.025,max_iter=i).fit(X,y)\n",
    "    score.append(lr.score(vali_x,vali_y))\n",
    "plt.figure()\n",
    "plt.plot([1,2,3,4,5,6],score)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1350ca278>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MTJnJruxd7M7e/ZchsqN1NFE7oji/6TxJPyVhPmXGzJ+T5cTFxfHAAw/Qtm1bYmJiDP/SEM61evVqevbsidls5siRI1itVsO7IEuhEMJJtNZ8dfYrPkn+hG/Pf3thvULRMKchpq0mkpYnkbwrmWSdfOH1pk2bEhsbS2xsLK1atZJuqWXIokWLePjhh7FYLDz++ONMnz4dT0/jv6aNTyCEm0nOS2be6XnMPTWXXdmOexV8tS+NjzYmeVEyyb8ksztj94Xta9WqRb9+/YiNjaV+/fqEhIQYFV0YaPr06QwZMgStNc899xzvv/9+qTmtKIVCiJvgXME5FpxewMrUlaxOW41VWwHwzPZELVbkrcpj07lNAAQFBXFPz3vo3Lkz7dq1o06dOnI6qYybPXs2gwcPBmD06NH8+9//LlU/E1IohLgOZpuZXzJ+YVvWtgvjKF247mAH1gNrwbrVChZo0KABjTo2YvDgwbRu3bpUnE4QpUfXrl1p2LAhQ4cOZejQoUbH+Rv5aRWimOzazrdp37LozCK+PP0lmbbMP1+0AVuB74HtQKpjhrXhrw3n4Ycfpnr16oZkFqWXzWYDwMPDg7CwMLZu3Vpq59iQQiFEEXJsOezP2U/8uXi+OPUFh3MPX3jN96Qvebvy4DfgZ6hWrhqxsbG0H9Ce1q1bU7ly5VJ1+kCUHvn5+Tz88MNERETwySefoJQqtUUCpFAI8TfZ1my+PPMlS84sYW3a2r90ZfU674VluQV+gbzf8wgMDKRmzZrMXDuzRMfeEa4rOzubnj17snbtWoKDg3nxxRepVq2a0bGKJIVCCBxdWdenr2fOqTnMOz3vwqxuSiuC04PJ3JUJq8CyzUJo+VB69+5Nzwk96dChAz4+PganF64iLS2Nrl278ssvvxAZGUlCQkKpLxIghUKUcWmWNOafms9nKZ+xLevPuRjCUsLIXJyJ5QcLmemOaxHR0dG88/k79O3bt1SfJhCl08mTJ+nYsSN79+6latWqrFmzhlq1ahkdq1ikUIgyJSU/ha2ZWzljOcP3579n0ZlF5NsdA+p553nDt1CwpIDUpFQA7rjjDu677z7i4uK49dZb5ZqDuC5Hjx6lQ4cOHD16lPr167N69WoqVapkdKxik0Ih3F6iOZGPkz9mZ9ZO1mesx6ZtF15TWuG7x5e8+DwKfiyAAmjRogWPjHyEbt26ERMTY1xw4TZCQkIoX748LVu2ZMWKFYSFhRkd6ZpIoRBuKduazdrza5l7ai6Lzyz+y2sVT1UkbX8aBScK0Ks0eSl51K9fn6fef4rY2Fjq1q1rUGrhrsqVK0dCQgJ+fn4EBQUZHeeaSaEQbkNrze7s3cSnxjM+aTxnLGcuvFbhfAXOLzpPwQ8FnDp96sL6du3aMeT9IfTt21fmbBA31apVq1i+fDmTJk3CZDIRGRlpdKTrJoVCuDyr3cqsU7N45dArfykO0eZosn/JJn1eOqePOIbybtmyJbFPxtKoUSPuvfde/Pz8jIot3NiCBQt45JFHsFqttG/fngceeMDoSDdECoVwWVa7lVVpq3j50Mvszdl7YX3DEw1JnJtI8mrHiKyhoaF0e6wbw4cPp3nz5kbFFWXE1KlTGTZsGFprRo4cSZ8+fYyOdMOkUAiXk2vL5bXDrzH31FxOFThOI4URRuSPkSR+mMjubMfIrG3atOGFF16gW7duclpJOJ3WmjFjxvDqq68CMGbMGF566SW36CknhUK4DLu2sy1rGy8efJHv078HINISiXmpmdQFqaRmObq0tmnThnHjxtGqVSsD04qy5I/Wwx+z0U2ZMoUhQ4YYHeumkUIhSjWr3cqenD1szdzKh0kf8pv5NwA8CzzxG+/HmdV/XpMYMmQIb7zxBhUrVjQqriijcnNz2bBhA15eXsyePZu+ffsaHemmkkIhSq0dWTsYsG8AO7J3XFjncd4D2w82rHOsZKVn0bhxY0aMGEGfPn0IDAw0MK0oy/z9/VmxYgW7du2iQ4cORse56aRQiFLndP5pXj/yOjNOzsCmbQToALx2epH+XTq21TaqV67OC2+/QNu2beVuaWGYrKwsPvnkE0aOHHlhqHB3LBIghUKUElprkvOTWXpmKW8dfYtUi+N6Q+0Dtfn9ld8hE8LDw/nX6H/x/PPPy8Q/wlCpqal06dKFLVu2kJGRwTvvvGN0JKeS/23CcPHn4hl2YBhJeUkX1gUfDibzk0x+3/E7fn5+vPPROzz11FP4+voamFQISE5OpmPHjvz2229Uq1aNgQMHGh3J6aRQCMOkWdJ488ibTDg+AYDypvIEnArg5JyTZK7KpFxQOXoN6MXIkSOpX7++wWmFgIMHDxIbG8uxY8e49dZbSUhIICoqyuhYTieFQpS4bGs27yW9x9ijYynQBSgUt+6+lf2v7Cc9Lx2TycRTQ55i9OjRhIaGGh1XCAB27NhBp06dOHPmDHfccQfx8fFl5udTCoUoMVprxhwbw1tH3iLPngdAucPlyJiQwe69jpvkevXqxRtvvEHDhg2NjCrE37z22mucOXOGjh07snTpUgICAoyOVGKkUIgSUWAvoP/e/iw6swgAU7oJ+3/sZOzOICgoiMefeZxhw4bJyK2i1Jo9ezbjxo3j9ddfL3OzGkqhEE6ltWbGyRm8c/QdjuYdReUq9DSN/Rs79evXZ/gnw+nfv79LDr0s3N/3339PmzZt8PT0pHz58m7fu+lKTEYHEO7rx/M/0mZbGwbvH8zRvKOQAnqkpnlKc9auXcuePXsYOnSoFAlRKk2ePJm77rqLQYMGobU2Oo6hpEUhbrrv0r5jUvIklp5d6liRDsyCqO1RvDvmXfr164fJJL+jiNJJa83bb7/Na6+9BkDdunXL/E2dUijETZNny+O5g88x9cRUxwo7MBdYBH3i+vD5gc/x9/c3MqIQRbLb7bzwwguMHz8ek8nE1KlTGTRokNGxDCeFQtwUWdYs4nbGsT59PSa7CfuXdvgGInQE85fP5+677zY6ohBFslqtPPnkk3zxxRd4eXkxb948evfubXSsUkHa/+KGWOwWJh+fTPAPwaxPXw95YH/BDp/C410fZ/fu3VIkhEsYM2YMX3zxBQEBAcTHx0uRuIi0KMR10Voz69QsXjr0EqcLHNOMkgFMhrbl2zLu53HccccdhmYU4lo8++yzbNy4kddff11+di8hhUJcs2xrNvftuo9159c5ViQB+0GNVwx8dCDTpk0r8xf/hGtITU0lKCgIb29vgoKCWLlypdGRSiU59SSKLc+Wx8TjE6m/qT7rzq9D5SgYCzwBQ7OGkrg/kenTp0uREC7h+PHjtGnThsceewybzWZ0nFJNWhSiWL4++zUvHXqJ382/A6CSFXqUplXlVny671Pq1atncEIhiu/AgQPExsZy/PhxvLy8yMjIKDPjNl0PKRSiSDm2HIbtH8asU7McK/KBsaB/1twXdx8LFiyQob+FS9m+fTudO3fm7NmztGrVivj4eEJCQoyOVarJqSdxRb9k/ELrra3/LBJLwaOnB/eF3seP637kq6++kiIhXMoPP/xA+/btOXv2LJ06dWLNmjVSJIpBWhTiskYfGc1rhx13pnIK+BBa+7Vm0i+TaNy4saHZhLgeGzdupHPnzuTl5dG3b19mzZqFt7e30bFcghQK8TcfJ37Ma0cLi8Q3UG5xOSa+M5F+/frJFKTCZTVu3JjmzZvToEEDJk+ejIeHh9GRXIZT/9crpToDEwAPYIbWeuwlr5cD5gBVCrO8r7X+zJmZxJWlWlJ5Yt0TLDctd5yU/BT62vvy3s/vUblyZaPjCXFdbDYbHh4e+Pv7k5CQgJ+fn/TMu0ZOKxRKKQ9gMhALJANblFLLtdb7LtrsaWCf1rqbUioCOKCUmqu1LnBWLnF5i5MW029nPyz+FgAqranEV899RYsWLQxOJsT10Vrz5ptvsnPnThYuXIinp6eMNXadnNmiaAkc0lofBlBKLQDuAy4uFBoIUo7yHgikAVYnZhKXMTVxKkMTh4I/qN8UA70GMvXtqdI0Fy7Lbrfz3HPPMXHiREwmExs3bqRdu3ZGx3JZziwUlYDjFy0nA7dfss0kYDlwEggC+mqt7ZfuSCk1GBgMUKVKFaeELasm7JnAs6efBQ/wW+PHjid2ULt2baNjCXHdLBYLTzzxBHPmzMHb25v58+dLkbhBzuwee7mTgJfO/tEJ2AFEAY2BSUqp4L+9SetpWuvmWuvmERERNz9pGWSz2Xhs6WM8m/IsAKHxoewdsleKhHBpubm59OrVizlz5hAQEMCKFSvo1auX0bFcnjNbFMnAxVdAo3G0HC42ABirHdNHHVJKHQHqApudmKvMO3nuJLd9eRupdVMBiNoWxbYXt1GxYkWDkwlx/TIzM+nWrRs//vgjoaGhrFy5kpYtWxodyy04s0WxBaillKqmlPIGHsRxmuliScDdAEqpCkAd4LATM5V5R44coeGUho4iYYFOBzqROCJRioRwed7e3nh6ehIVFcX69eulSNxETmtRaK2tSqnhQAKO7rEztdZ7lVJPFb4+FXgL+FwptRvHqaqXtNbnnJWprNu+bzttl7XFfKcZgPGR4/ln538anEqIm8PX15evv/6atLQ0qlatanQct6JcbdLw5s2b661btxodw6VorXn5lZcZ5zMO2oEpz8SntT7l8dqPGx1NiBuyf/9+3n//fT755BO5y/oqlFLbtNbNr+e9cpttGTD186mMo7BI2EysvHUlHat2NDqWEDdk69atdOnShXPnzhETE8OoUaOMjuS2ZFBAN/fm9DcZFjgMYsHT7smSJkukSAiX991339GhQwfOnTtH165def75542O5NakReGm7NrOkLVDmFFpBvg41q1supJ7wu4xNpgQN2jZsmX07duX/Px8+vXrx+eff46Xl5fRsdyatCjckNVuZeD2gcwwOYpEpaOVWN9svRQJ4fJmzZrF/fffT35+PsOGDWP27NlSJEqAtCjczLmCc9TYWINMWyZYIWJJBAc+OkBAQIDR0YS4IVprFi5ciM1mY9SoUbz55psyuF8JkULhRrTWNPuxGZkqE9IhamkUm8dtliIh3IJSioULF7Js2TIeeugho+OUKXLqyU3k2/Npt7YdSSoJgIgpEWwau4lKlSoZnEyI62e325k0aRJ5eXkA+Pv7S5EwgBQKN5BdkM0dX9/BBtMGyIH2G9pz7JtjMoeEcGkWi4X+/fvzzDPPMGDAAKPjlGly6snF2e12aiyowZlKZ8ACXTd25X9j/ifnboVLM5vN9OnThxUrVhAYGMjgwYONjlSmSYvCxfWd3NdRJHLhlXOv8M0730iREC4tPT2dTp06sWLFCsLCwli3bh0dOnQwOlaZJi0KF5WXl0fPsT1ZdfsqADqpTrzT/x2DUwlxY06fPk3nzp3ZsWMHlSpVYs2aNdSrV8/oWGWeFAoXlJeXR9N/NuW3nr+BF/wj+x/Ed483OpYQN2zMmDHs2LGDWrVqsWbNGhncr5SQQuFicnJyaPhcQ470PgIe8KDHg8zrPk9ONwm3MHbsWGw2G6+99hqRkZFGxxGF5BqFi3l06qMc6esoEoN8BzHvH1IkhGvbuXMnZrNj6HtfX18+/vhjKRKljBQKF/L54s9ZWnspAHdb7mZa62lSJIRLW7t2La1bt6ZPnz5YLBaj44grkELhIt59910GbB8A/hCcFcyiuxYZHUmIG7J06VLi4uLIyckhJCTE6DiiCFIoXMDo0aN5efnLcDd4Wb3YEruFEG/5jyVc18yZM+nTpw8FBQU888wzzJo1Swb3K8WkUJRymzdv5v8m/R+MdCy/X/99agfUNjaUEDfggw8+YODAgdjtdl5//XUmTJiAySRfRaWZ9HoqxZKTk+nYqSP63xqCoUtYF4ZHDzc6lhDXbeHChYwc6fitZ8KECYwYMcLgRKI4pFCUUnl5edx3331kdMmAplDeozxf1P8Ck5LfvITr6tGjB927d6d379488sgjRscRxSSFopT65z//yfac7fAYKBRfNPiCCO8Io2MJcc0KCgooKCggMDAQb29vvv76a+mt52KkUJRCW7duZdq0afAF4AlPRD1B94juRscS4pqZzWbuv/9+LBYL8fHx+Pj4SJFwQXIeo5TJycmhd+/eUIfvQ/IAACAASURBVAOoBCZMfFjrQ6NjCXHNzp8/T2xsLKtWrWLnzp0cPXrU6EjiOkmhKEW01jRs2JBj1mPwH8e6hyo+RLBnsLHBhLhGp06don379mzcuJHKlSuzYcMG6tSpY3QscZ3k1FMpMn36dI6kH4EpQDjcHnw7E2tPNDqWENfkyJEjxMbGkpiYSN26dVm9erVMouXipFCUEvn5+QwfPhz+BYRDZZ/KfNv0WwI8ZL5r4TqOHTtG69atSUlJoVmzZqxcuZKICOmE4eqkUJQSTz/9NJbqFrjLsfxNo2+kSAiXU6lSJVq2bElGRgbLli0jOFhOm7oDKRSlwJQpU/j0y0/hbcfyyCojaRTUyNhQQlwDrTVKKTw9PVmwYAHgGAlWuAe5mG2wCRMmMGz4MHgDaAARXhE8W/lZo2MJUWxLliwhNjb2L0OFS5FwL1IoDLRjxw6ee+456Ak0hgpeFdjSYguVfCsZHU2IYpkxYwYPPPAA3377LfPmzTM6jnASKRQG0Vrz/PPPo6tpPAZ7ADCl7hSq+snUj8I1jBs3jkGDBmG323nzzTcZOHCg0ZGEk8g1CoNMnz6d7zZ+h2mSCZuHjV4RvegR0cPoWEJcldaaV155hXfffReASZMm8fTTTxucSjiTFAoDnDt3jldffxVeBXs1OzX9ajKz/kwZ2kCUejabjaFDhzJ9+nQ8PT354osv6Nevn9GxhJNJoShhFouF+++/n3NdzsGd4G/yZ+ltSynnWc7oaEIUS2ZmJr6+vixevJi4uDij44gSIIWihM2ZM4cf03+Ehx2jwi5vtJyGgQ2NjiVEsXh4eDBr1iz27t1LkyZNjI4jSohczC5BK1eu5KlnnoLCuVqer/I8d4febWwoIa4iLS2NoUOHkpWVBYC3t7cUiTJGWhQlZN++fcTFxaGHaIiBMM8w3qj2htGxhCjSyZMn6dSpE3v27CE3N5fPP//c6EjCAMVuUSilZDyJ66S15qWXXkKX03CvY92KxisI9Aw0NpgQRUhMTKRNmzbs2bOHunXrMnr0aKMjCYNctVAope5USu0DfitcbqSU+sTpydzI7Nmz+d///ofpQRP4QrfwbrQs19LoWEJc0e7du2nTpg1HjhyhefPmrF+/nujoaKNjCYMUp0XxEdAJSAXQWu8E2jkzlDuxWCyMGzcOyoNnL8eZvtervW5wKiGubOPGjbRr145Tp05x1113sW7dOsLDw42OJQxUrFNPWuvjl6yyOSGL29FaM2TIEPbu3Yv/o/4UmAq4N/xemgU3MzqaEFc0Z84c0tPT6dGjB/Hx8QQFBRkdSRisOBezjyul7gS0UsobR5+d35wbyz2MHz+ezz77DL8oP3zv88WszYysMtLoWEIUaeLEidx6660MHjwYT0/p7yKK16J4CngaqAQkA42BYc4M5S5mzpwJQNupbUnTabQIbkHb8m0NTiXE3y1cuJCMjAwAPD09GTZsmBQJcUFxCkUdrfXDWusKWutIrXV/oJ6zg7m65ORk9uzZg6maiW/9v8WEiWl1p2FScuuKKD201owdO5a+ffvSrVs3rFar0ZFEKVScb62Pi7lOFLJarfTq1QuAkNdCsGFjcKXBNA5qbHAyIf6ktebFF1/klVdeQSlFv379pBUhLuuKPxVKqVbAnUCEUur5i14KBjycHcyVTZs2jS1bthD4bCCpVVIJ9wrnrepvGR1LiAtsNhtDhgzh008/xdPTk9mzZ/Pggw8aHUuUUkX9+uANBBZuc3G3h0ygtzNDubLdu3fz7LPPgg8UdC0A4LP6nxHuLd0LRemQn5/Pww8/zJIlS/Dz82PJkiV06dLF6FiiFLtiodBa/wD8oJT6XGt9rAQzuSyr1UrPnj2xWCzUnFSTQ6ZDtAhuwb3h9xodTYgLpk2bxpIlSyhXrhzx8fG0bt3a6EiilCvOCUmzUuo9oAFwYSJcrfVdTkvlovbs2UNiYiJBbYM4VPcQPiYf3q/5vtGxhPiLYcOGceDAAQYNGkSjRo2MjiNcQHEKxVzgSxyjFD0FPAacdWYoV/XZZ5+BCfQLGoBRMaNoFyI3sQvjnTx5Eh8fH8LCwvDw8GDSpElGRxIupDi9nsK01p8CFq31D1rrJ4A7nJzL5WRmZjJx4kR4ALIDs/Ex+fBcleeMjiUEhw4donXr1sTFxZGdnW10HOGCilMoLIV/piil4pRSTQAZHewSQ4cOhQjw6O/oEPafav8hwEMG3BXG2rlzJ23atOHo0aNorSkoKDA6knBBxTn1NFopVQ54Acf9E8HAs05N5WLGjx/PvHnzYDjYfG3EhsbyYtUXjY4lyriffvqJuLg4MjIyuPvuu/n6668JDJSh7cW1u2qLQmv9P611htZ6j9a6g9a6GZBWnJ0rpTorpQ4opQ4ppV6+wjbtlVI7lFJ7lVI/XGN+wxUUFDBq1CjwhHJdHPNej4oZhVLK4GSiLFu5ciWxsbFkZGTQq1cv4uPjpUiI61bUDXcewAM4xnhapbXeo5S6F/g34AcUORdi4fsnA7E4xojaopRarrXed9E25YFPgM5a6ySlVOSNfqCSNmPGDHJycqgwsAKnfU5Tz78erctLd0NhnG3bttG9e3esVisDBw5k6tSpcse1uCFF/fR8ClQGNgMTlVLHgFbAy1rrr4ux75bAIa31YQCl1ALgPmDfRdv0A5ZqrZMAtNZnrv0jGOfs2bP85z//AcCjt+PaxP9V+z88lNy4LozTpEkTHn74YSIjI3n33XeldStuWFGFojlwm9barpTyBc4BNbXWp4q570rAxfNYJAO3X7JNbcBLKfU9jru/J2itZ126I6XUYGAwQJUqVYp5eOcbNWoUZ86coWLPipz0Okk5z3L0jOxpdCxRBmmtycnJITAwEJPJxMyZMzGZZABKcXMU9ZNUoLW2A2it84Dfr6FIAFzu1xh9ybIn0AyIwzGL3mtKqdp/e5PW07TWzbXWzSMiIq4hgvMcOnSImTNnokwK7xHeAPSv2B8fk4/ByURZo7Vm5MiRtG3b9sJQ4VIkxM1UVIuirlJqV+FzBdQoXFaA1lrfdpV9J+M4dfWHaODkZbY5p7XOAXKUUj8CjYDfi/sBjDJhwgSsVivtR7Xne9v3RPlEMb7WeKNjiTLGarUyePBgPvvsM7y8vNi8eTOxsbFGxxJupqhCcaNzTmwBaimlqgEngAdxXJO42DJgklLKE8cghLfjmKO7VLNYLCxZsgQUHOlwBDQMvGUgnia5YChKTl5eHg899BBff/01/v7+LF26VIqEcIqiBgW8oYEAtdZWpdRwIAHHsOQztdZ7lVJPFb4+VWv9m1JqFbALsAMztNZ7buS4JWHKlCmkpKQQ+nAox/QxonyieDnmsr1/hXCKrKwsevTowbp16yhfvjzx8fHceeedRscSbsqpvwJrrVcAKy5ZN/WS5feA95yZ42abM2cOAH79/QB4rOJj+Hv4GxlJlCHZ2dncfffdbNmyhYoVK5KQkMBtt13tTLAQ10/OlVyj+Ph4tmzZgmd9T054nSDII0haE6JEBQQE0LRpU1JTU1mzZg3Vq1c3OpJwc8UqFEopP6CK1vqAk/OUavn5+YwcORKAqH9FkUQST0Q9QbBnsMHJRFmilGLy5MmcP3+e8HCZEEs431X70CmlugE7gFWFy42VUsudHaw02rJlC/v374d6kFQ5CW/lzdBKQ42OJcqAX3/9lbvuuovU1FQAPDw8pEiIElOcztZv4LjLOh1Aa70DiHFepNLr22+/BaDqiKoAvFDlBeoE1DEykigD1q9fT/v27fnuu+8YM2aM0XFEGVScQmHVWmc4PYkLmD9/PgDZNR1j+j9yyyNGxhFlQHx8PB07diQzM5M+ffrw9ttvGx1JlEHFKRR7lFL9AA+lVC2l1MfARifnKnVsNhuJiYnQCFJVKhW9K1LHX1oTwnnmzp1Ljx49yMvLY9CgQcyfPx8fH7nzX5S84hSKZ3DMl50PzAMyKIPzUSQkJGC1WvHv4ugGOzBqICYlwyQI55g0aRL9+/fHarXy8ssv89///hcPDxlsUhijOL2e6mitXwVedXaY0mzMmDEQALq1Y7iqjqEdDU4k3Nnhw4cBePfdd3nxRZkESxirOIXiQ6XULcAiYIHWeq+TM5U6x44dY+PGjfAE5Prl0jK4pcw5IZzq/fffp3v37rRv397oKEIUa4a7DkB74CwwTSm1Wyk1ytnBSpP//ve/2APtjtGqgEl1JsmcE+Kmslqt/Pvf/+bMGceULCaTSYqEKDWKdZJda31Kaz0ReArHPRX/59RUpciZM2eYMGGCo1QCLYJb0CK4haGZhHvJy8vj/vvvZ8yYMfTu3RutLx2NXwhjFeeGu3pKqTeUUnuASTh6PEU7PVkp8f7772M2mwnqEwTAk1FPGpxIuJPMzEy6dOnC8uXLCQkJ4b333pMZ6USpU5xrFJ8B84GOWutL55Nwa9nZ2UyZMgWqQdYtWZT3LM8jFeXeCXFznD17li5durBt2zaioqJYvXo1DRo0MDqWEH9z1UKhtb6jJIKURl999RXZ2dnc8uItpJBCvwr98PPwMzqWcANJSUl07NiRAwcOUKNGDdasWUO1atWMjiXEZV2xUCilFmqtH1BK7eavU5gWd4Y7l/foo4+CJ2S2zATgiagnDE4k3MX8+fM5cOAAt912GwkJCVSsWNHoSEJcUVEtin8W/nlvSQQpbcxmM56enlhbWcnxzOG2wNtoGtTU6FjCTbz44ot4eXkxYMAAQkJCjI4jRJGueDFba51S+HSY1vrYxQ9gWMnEM86vv/6K1WolsEsgAP0q9JOLjOKGbNiwgZMnHZf5lFI8//zzUiSESyhO99jLTcLb5WYHKW3Gjx8PYWBubsaEid6RvY2OJFzY8uXLueeee+jYsSMZGTLGpnAtRV2jGIqj5VBdKbXropeCgJ+cHcxIW7ZsYcmSJajRCrvJTp/IPtTwr2F0LOGiZs+ezYABA7DZbLRt25bAwECjIwlxTYq6RjEPWAmMAS6e6zNLa53m1FQGmzp1KtpP49HcAxs23qz+ptGRhIuaMGECzz7rGEPz1Vdf5a233pJTmMLlFHXqSWutjwJPA1kXPVBKhTo/mjHS0tKYOXMmtAabh42mQU2pG1DX6FjCxWiteeONNy4UiQ8++IDRo0dLkRAu6WotinuBbTi6x178E64Bt5zRff369QB4xDlaE4OjBhucSLiihIQE/vOf/2AymZgxYwYDBgwwOpIQ1+2KhUJrfW/hn2XqLqAdO3ZAE7DdasNbefNgxQeNjiRcUKdOnfjXv/5Fq1at6Nmzp9FxhLghV70zWynVGtihtc5RSvUHmgLjtdZJTk9XwqxWK7Nnz4Z+juWh0UMp51nO2FDCZeTm5nL+/HmioqJQSjFu3DijIwlxUxSne+wUwKyUagS8CBwDZjs1lUFmzpxJYk4i/MOxPDx6uLGBhMvIyMigU6dOdOjQ4cJQ4UK4i+IUCqt2jHt8HzBBaz0BRxdZtzN79mxo6XjeLbwbNf1rGhtIuITTp0/Tvn171q9fT05ODmlpbt0pUJRBxSkUWUqpV4BHgHillAfg5dxYJS81NZVNmzZB4RCI3cO7GxtIuIRjx47Rtm1bduzYQc2aNdmwYQN160ovOeFeilMo+gL5wBNa61NAJeA9p6YywAcffIDVbsWjkWPmuq7hXQ1OJEq73377jTZt2nDw4EEaN27Mhg0biImJMTqWEDddcaZCPQXMBcoppe4F8rTWs5yerARZrVbmzp0L3cHmY6O6X3WifKKMjiVKsdOnT9O2bVuSk5Np06YN3333HRUqVDA6lhBOUZwZ7h4ANgN9gAeAX5RSbjXw0cSJE0lKSsLnHh8AhlYaanAiUdpVqFCBQYMG0bVrVxISEihfvrzRkYRwGnW1+XmVUjuBWK31mcLlCGCt1rpRCeT7m+bNm+utW7fetP1lZ2dTq1YtTplP4bHEA5uycbbtWcK9w2/aMYT7yM/Px8fH8QuF1hqbzYanZ3EmihTCWEqpbVrr5tfz3uJcozD9USQKpRbzfS7hpZde4tSpU0TeH4lN2egU2kmKhLiszz//nIYNG/5lqHApEqIsKM4X/iqlVIJS6nGl1ONAPLDCubFKRlZWFtOnTweg2uOOG9D7VuhrZCRRSn300UcMGDCAgwcP8vXXXxsdR4gSVZw5s/+llOoFtMEx3tM0rfVXTk9WAvbu3YvFYqFBmwZssWzBS3nRI6KH0bFEKaK15rXXXuPtt98GHAVj2DC3n7dLiL8oaj6KWsD7QA1gNzBSa32ipIKVhIULFwIQ2CUQO3a6hnUlxEtmHBMOdrud4cOHM2XKFDw8PJg5c6ZjHnUhypiiTj3NBP4H3I9jBNmPSyRRCbHb7cybNw+AglYFAPSNlNNOwsFut9O/f3+mTJmCj48PS5culSIhyqyiTj0Faa2nFz4/oJTaXhKBSsqePXs4ffo0UQ2i2GHfgbfypnuE3I0tHEwmE3Xr1iUoKIjly5fTvn17oyMJYZiiCoWvUqoJf85D4XfxstbapQvHnDlzAKj0UCVOcpK7Q+8m2DPY4FSiNHnttdd4/PHHqVKlitFRhDBUUYUiBfjwouVTFy1r4C5nhXK2jIwMxo8fD0D+nflgh7iwOINTCaOdPn2aJ598ksmTJ1OlShWUUlIkhKDoiYs6lGSQkrRhwwYsFgsN2zZkr96LCRMPVHjA6FjCQEePHiU2NpZDhw6hlGL58uVGRxKi1HCbG+euxa5duwCo2KMiNm2jY1hHIrwjDE4ljLJv3z5at27NoUOHaNKkCTNmzDA6khClSpksFIcPHwbgdLXTgAwpXpZt3ryZtm3bcvLkSdq1a8d3331HZGSk0bGEKFXKZKHYu3cveMChoEMAdA2TIcXLorVr13LXXXeRlpbGvffey6pVqyhXTqa+FeJSxRk9Viml+iul/q9wuYpSqqXzozlHVlYWW7ZsgZZgVmbq+dejql9Vo2MJA+zcuZOcnBz69+/P0qVL8fPzMzqSEKVScUY0+wSw4+jl9CaQBSwBWjgxl9N8++23WK1WIu6J4CxnaVO+jdGRhEFeeOEFateuTVxcHCZTmWxcC1EsxfnfcbvW+mkgD0BrfR7wdmoqJxo9ejSYIPvObACejHrS4ESiJH3yySckJiZeWO7WrZsUCSGuojj/QyyF82RruDAfhd2pqZxk/fr1bNu2jYDmAeR65VLTryYtgl2yYSSukdaaV199laeffppOnTqRl5dndCQhXEZxCsVE4CsgUin1NrABeMepqZxk7ty5AFR7xjGkeFx4HEqpot4i3IDNZmPo0KG88847eHh48MYbb+Dr62t0LCFcRnGGGZ+rlNoG3I1j+I4eWuvfnJ7MCTZt2gRATlQOaOgQ4rb3FIpCBQUFPProo3z55Zf4+vqycOFCunXrZnQsIVzKVQuFUqoKYAa+uXid1jrJmcFuttzcXHbv3o0KUxzRRwCIDY01OJVwppycHO6//34SEhIIDg7mm2++oV27dkbHEsLlFKfXUzyO6xMK8AWqAQeABk7MddP9+uuv2O12IntHcoYzdArthL+Hv9GxhBPFx8eTkJBAREQEq1atomnTpkZHEsIlFefUU8OLl5VSTYEhTkvkJOvWrQPAfLsZQIYULwMeeOABUlJS6Ny5M3Xq1DE6jhAu65pnhtdab1dKuVxXoZUrV0Ig5FTOAaBbuJyndkdHjhyhoKDgQmH45z//aXAiIVxfca5RPH/RogloCpwtzs6VUp2BCYAHMENrPfYK27UANgF9tdaLi7Pva5Gfn8/27duhBWil+Uf5f1DZt/LNPoww2J49e+jYsSOenp789NNPVK4s/8ZC3AzF6R4bdNHDB8c1i/uu9qbCey8mA12A+sBDSqn6V9juXSCh+LGvzebNm8nLyyMk1jEfdsewjs46lDDIpk2baNeuHSkpKdSoUUPGbBLiJiqyRVH4JR6otf7Xdey7JXBIa324cF8LcBSYfZds9wxOHhLku+++A8Da2ApAp9BOzjqUMMCaNWvo0aMHZrOZ7t27X+gKK4S4Oa7YolBKeWqtbThONV2PSsDxi5aTC9ddfIxKQE9g6nUeo1g2b94M0ZDln0W4VzhNgpo483CiBC1evJi4uDjMZjOPPfYYS5YskSIhxE1WVItiM44isUMptRxYBOT88aLWeulV9n25W571JcvjgZe01rai7pBWSg0GBgPXPDVldna2o0VROHHrPaH3YFIyto87+P333+nbty92u51nn32WDz74QMZtEsIJitPrKRRIxfFV+8f9FBq4WqFIBi6+mhgNnLxkm+bAgsIiEQ50VUpZtdZfX7yR1noaMA2gefPmlxabIs2dOxez2UxIuxDOc55bA269lreLUqx27dqMHTuW/Px8Xn31VRmORQgnKapQRBb2eNrDnwXiD8X5st4C1FJKVQNOAA8C/S7eQGtd7Y/nSqnPgf9dWiRu1MaNGwHwrO/4qNX8qhW1uSjltNakpKQQFRUFwL/+dT2Xz4QQ16KodroHEFj4CLro+R+PImmtrcBwHL2ZfgMWaq33KqWeUko9daPBiyMvL49ly5aBB2T7OYYVl/knXJfNZmPw4ME0a9bswnS2QgjnK6pFkaK1fvNGdq61XgGsuGTdZS9ca60fv5FjXc6PP/5IRkYG1e+tzmF9mLr+danie23XOETpkJ+fT//+/Vm8eDG+vr4kJiZSvXp1o2MJUSYU1aJw+RO+EyZMAKB6F8cXyp3l7jQyjrhO2dnZdOvWjcWLFxMcHMzq1auJjZUBHYUoKUW1KO4usRROkJqaysqVK/H29ib49mBIh2bBzYyOJa5RWloacXFxbNq0icjISFatWkWTJtK9WYiSdMVCobVOK8kgN9uWLVvQWlOzVk1+Nv8MQPvy7Y0NJa5Jfn4+7du3Z/fu3VStWpU1a9ZQq1Yto2MJUea4bafzxYsdQ0Y1ursRKQUphHmFUS+gnsGpxLXw8fFh0KBB1KtXjw0bNkiREMIgblsoDhw4AEDDbo5R0m8NuFX62bsIq9V64fkzzzzDtm3biI6ONjCREGWbWxaKgoICx2ixwNHwowA0DGxYxDtEabFx40bq16/P/v37L6zz8/MzMJEQwi0Lxfbt2zGbzdSrVw+rt+O30/Ke5Q1OJa5m1apV3HPPPRw8eJCPP/7Y6DhCiEJuWSiOH3eMRVi3bl0ScxMB6Rpb2n355Zd0796d3NxcBgwYcKFrsxDCeG5ZKFJTUwEIDQvl16xfAWTE2FLsv//9Lw899BAWi4UXXniBTz/9FE/Pa558UQjhJG5ZKFJSUgDwqupFpi2TW7xvoaJPRYNTicsZO3YsTz31FFpr3nnnHd577z3pdCBEKeOWv7bt2rULAI96HoDcaFeahYWFYTKZmDx5Mk89VSJDgAkhrpHbFQqLxcKqVasAKKhaABnQLEgKRWk1aNAg2rZtS926dY2OIoS4Arc79bR161by8vKoU6cOiSbHhWwpFKVHfn4+AwcOZPfu3RfWSZEQonRzu0Kxbt06AO66+y62ZznupWgadL2zuYqbKSsri7i4OGbOnEnfvn2x2WxGRxJCFIPbnXr6Y6Ki2v+oTbo1nQreFYjyiTI4lUhNTaVr165s3ryZChUqMH/+fDw8PIyOJYQoBrdqUWRnZ7NmzRoAvOp7AY7TTtKLxlgnTpygXbt2bN68mZiYGDZs2ECjRo2MjiWEKCa3KhSHDh3CYrFQr149jvs6brqT007GOnjwIK1bt2bfvn00aNCADRs2ULNmTaNjCSGugVsVij+mx6xZsyaLziwCpGus0bZv386xY8e4/fbb+fHHH6lUqZLRkYQQ18itrlEkJjp6OVWrXo31lvUANAmUO7KN1LdvX3x8fLjnnnsIDLzqVOtCiFLILVsUgXUDSbemU9G7osyRbYCVK1eybdu2C8s9evSQIiGEC3OrQrF27VoAPGs5GkpyIbvkzZ8/n+7du9O5c2dOnDhhdBwhxE3gNoXCYrFw6NAhlFKYqjo+lsxoV7KmTJnCww8/jNVqZcCAAURFSbdkIdyB2xSKP2a0q1SpEr/n/Q5IoSgpWmvefvtthg0bhtaasWPHMm7cOGnNCeEm3OZi9pYtWwDHcBC/mX8DpFCUBLvdzsiRI/noo49QSjF16lQGDx5sdCwhxE3kNi2KzZs3A9D+rvYcyHG0Lur5S6Fwtq1btzJ+/Hi8vLxYsGCBFAkh3JDbtCgOHToEQECtAMx2M9E+0ZT3kulPna1ly5bMmDGDSpUq0alTJ6PjCCGcwO0KRVb5LADqB9Q3Mo5by8rK4vDhwxeG4XjiiScMTiSEcCa3OPVkt9tJSkoCwCfGB4Aa/9/enUdHVWWLH/+eTGQAQhhlNEwxkAkkIMgMEhAQRRAcGRa0NnSLrYL67PdoGrr9qdBPm8ahUWlREWzBAIrMDoEYWgbDGAR/jGEMSQgEE0lS+/1xK0VCihBiKpUK+7NWrdRw6t5dJ0nte++5Z9+A1u4Mqdo6d+4c/fr1o2/fvsVKhSulqq9qkShSUlKw2Ww0a9aM1IJUQBOFKxw/fpyePXuybds2QkJCCAoKcndISqlKUC0SReGWbfv27Tnws3VqbJtALTxXkQ4cOECPHj3Yv38/kZGRbN68mVatWrk7LKVUJagWiaKwdEdUVBRHco8AEOof6r6AqpkdO3bQo0cPjh07Rrdu3UhISKBx48buDkspVUmqRaI4e/YsALc0uYVDOVbSaBvY1p0hVRtZWVkMGDCAtLQ04uLiWL9+PSEhIe4OSylViapFokhJsSbY+TX2I0/yaOTXiEDvQDdHVT0EBwfz+uuvM3r0aFauXKnjEkrdhKpFojh69CgA3o2tS2tqxdhf79y5c477jz32GIsXL6ZGjRpujEgp5S7VIlH88ssvAOSG5ALQvEZzd4bj8f7xj3/Qpk0bduzY4XhO6zYpdfOqFoni559/BmB3vnX2U2ztWHeG47FEhD//uS1mUgAAIABJREFU+c9MmTKFrKwskpKS3B2SUqoK8PiZ2WlpaZw9e5agoCAOiTWQHVtLE8WNstlsPP3008ydOxcvLy/mz5/PhAkT3B2WUqoK8PhEsXLlSgC6devGvtx9gM6huFF5eXlMmDCBDz/8ED8/PxYvXsz999/v7rCUUlWExx962r9/PwA97urByV9O4mN8dIziBogIDz/8MB9++CFBQUGsWrVKk4RSqhiPTxRpaWkA5DaxBrLbB7XHx8vjd5QqjTGGUaNG0aBBAzZu3Mhdd93l7pCUUlWMx3+jFk62u1D/AgAdanZwZzgeQ0QcZzI98MADDBw4kNq1a7s5KqVUVeTxexSF5cVPBJ4AIDwo3J3heIRjx44RGxtb7KwmTRJKqWvx6ERx+fJlDh48iJeXF+mB6QB0rt3ZzVFVbT/++CM9evRgx44dvPjii4iIu0NSSlVxHp0oCg87NWzYkEO/WKfGtgnQM56upbC43/Hjx+nevTvx8fE6kU4pdV0enSgKDzs1b9Wck7+cxNt409xfz3hy5ttvv6VPnz6cO3eOu+++m3Xr1lGnjl4qVil1fR6dKJKTkwEI7RoKgBdeeBtvN0ZUNX3++ecMHDiQixcv8uCDD7J8+XICA7VoolKqbDw6UZw4YQ1gB7QNAKBjrY7uDKfKEhHy8/OZNGkSH330EX5+fu4OSSnlQTz69NjCORQ/17VqPXWp3cWd4VRZw4YNY+vWrXTo0EHHJJRSN8yj9yhOnz4NQE5wDqDXyS4kIsyaNYuEhATHcx07dtQkoZQqF4/eo8jIyADgQo0LUIAOZGMV93vqqaeYN28eISEhHD58mODgYHeHpZTyYB6dKDIzMwE4baw9i5v9Otl5eXmMHz+eRYsW4efnx4IFCzRJKKV+NY9OFBkZGeAFB/MPAhAWGObmiNwnJyeHBx54gFWrVlGzZk1WrFhBv3793B2WUqoa8NhEkZubS0ZGBl5NvbBho75vfWr51HJ3WG6RlZXFPffcw6ZNm6hXrx6rV6+mc2edoa6UqhguHcw2xgwyxvxojPnJGPOCk9cfMcbsst++M8bElHXZx48fB6BBuwYA/GL7paLC9jg7d+5ky5YtNG3alISEBE0SSqkK5bI9CmOMN/AGMABIBbYaY1aKyL4izQ4DvUUk0xhzNzAfuKMsyz969CgAtdvX5gxnGNZgWIXG70l69erFsmXLiIqKIjQ01N3hKKWqGVceeuoC/CRiXZ/UGLMEuBdwJAoR+a5I+y1As7Iu/NAhq7aTf0t/AFr5t/rVAXuSffv2cfr0acc4xD333OPmiJRS1ZUrDz01BY4XeZxqf+5aJgCry7rwgwetAWzT2Job0DKg5Q0H6Km2bt1Kr169GDZsmKOMiVJKuYorE4Wz2V1Oa1obY/piJYrnr/H648aYbcaYbYWzsQsLAhZOtrtZEsVXX31Fv379SE9Pp0+fPoSF3bxneimlKocrE0UqUHQGXDPg5NWNjDHRwLvAvSKS7mxBIjJfRGJFJLZBA2vw+tixYwBk+FqT7lr6V/9EsXz5cu6++26ys7N5+OGHiY+P1+J+SimXc2Wi2Aq0Nca0NMb4AQ8CK4s2MMa0AD4DHhORAzey8LS0NPCDdNLxMT408y/z8IZH+te//sWIESO4fPkyv//97/nwww/x9fV1d1hKqZuAywazRSTfGPN7YC3gDSwQkb3GmN/aX38bmA7UA9601yHKF5HYsiz/+PHj0MK638K/RbUuL37y5EkmT56MzWZj+vTpzJgxQ+s2KaUqjUsn3InIl8CXVz33dpH7E4GJ5Vgu3t7eFNxSAFT/w05NmjRhyZIlHDlyhKeeesrd4SilbjIeOTM7OzubgoICvJt5U0BBtRzIttls7N69m5gYaw7ivffe6+aIlFI3K48sM154waKg1kFA9Tvj6fLlyzzyyCN07dq1WKlwpZRyB4/coyg8Rda0ts+hqEaHnn7++WdGjhzJ6tWrqVWrFjabzd0hKaVuch6ZKM6ePQvAhZYXAAgNCHVjNBXn/PnzDB06lMTEROrXr8+aNWvo1KmTu8NSSt3kPDJRnDlzBgDxtubvNavh+afGnj59mkGDBrFz506aN2/OunXrCA8Pd3dYSinlmWMUZ8+eBQNGrENPjfwauTmiX8dmszmSRFhYGJs3b9YkoZSqMjw3UTQEMcItfrfg5+Xn7pB+FS8vL1555RW6du3Kpk2baNGihbtDUkopB49MFOnp6VDHut+kRhP3BvMrXLx40XF/4MCBJCYm0rBhQzdGpJRSJXlkosjMzIRQ634D3wZujaW8NmzYQGhoKOvXr3c85+Xlkb8OpVQ155HfTJmZmY5h+EsFl9wbTDksW7aMIUOGkJGRwbJly9wdjlJKlcojE8X58+ehrnW/d0hv9wZzg9577z1GjRrF5cuXefLJJ3nzzTfdHZJSSpXKIxPFxYsXHYniFr9b3BvMDZgzZw4TJ07EZrMxY8YM/v73v+vhJqVUleeR8yhycnIgxLrvKYli1qxZTJ8+HYC5c+fy5JNPujkipZQqG49MFLm5uVZxcqBxjcbuDaaMevbsSc2aNXnrrbd49NFH3R2OUkqVmUcmisuXL3vEHoWIOK4b0adPHw4fPkz9+vXdHJVSSt0YjztA7iiSZ9+juKVG1UwUly5d4p577mHVqlWO5zRJKKU8kcftUeTn50NNwBdqedciyDvI3SGVkJGRwdChQ0lKSmLPnj3cdddd1KhRw91hKaVUuXhmogi27tf3rXpb6KdOnSIuLo49e/bQokUL1q1bp0lCKeXRPO7QU0FBAdjzQx3fOu4N5iqHDh2iR48e7Nmzh/DwcDZv3kxYWJi7w1JKqV/FM/coAqz7mXmZ7g2miN27dzNw4EBOnTpFbGwsq1ev1jGJcsrLyyM1NdU6u00pdUP8/f1p1qwZvr6+FbZMj0sUubm5YK+bN6DuAPcGU8TFixc5f/48ffv2ZcWKFdSqVcvdIXms1NRUatWqRWhoqOOsMaXU9YkI6enppKam0rJlxV350+MSxeXLlx2VY6vSdSjuvPNOvv32W6KiovD393d3OB4tNzdXk4RS5WCMoV69eo7LRVcUz0wU9jkUDf3cW5J76dKlGGMYMWIEAJ07d3ZrPNWJJgmlyscV/zselyjy8vIcexTunGz3zjvv8Nvf/hYfHx927drFbbfd5rZYlFLKlTzurCebzQb2w/91feu6JYZXXnmFxx9/HJvNxvTp0/XMpmrI29ubDh06EBkZyT333GNVLLbbu3cv/fr1IywsjLZt2zJr1ixExPH66tWriY2NpV27doSHhzN16lR3fIRyeeihh4iOjua1114rU/uaNWu6JA4RYcqUKbRp04bo6Gh27NhxzXb9+vXjwoULLomjIixcuJC2bdvStm1bFi5c6LTNsWPH6Nu3Lx07diQ6Opovv/zS8Vrh32KHDh0YNmyY4/kHH3yQgwcPujx+wOpoT7r5+voK7yJsQHZe2CmVyWazyXPPPSeAGGPkzTffrNT13yz27dvn7hAkKCjIcX/MmDHyl7/8RUREfv75Z2nVqpWsXbtWREQuXbokgwYNknnz5omIyO7du6VVq1aSkpIiIiJ5eXnyxhtvVGhseXl5Fbq8QqdOnZIWLVrc0HuK9lNFWrVqlQwaNEhsNpskJSVJly5dnLb74osv5A9/+MMNLTs/P78iQiyT9PR0admypaSnp0tGRoa0bNlSMjIySrT7zW9+4/g+2bt3r9x6662O167Vx998841MnDjR6WvO/oeAbVLO712P26MoKCiwZmYD9XzrVep6H3/8cV599VV8fHxYtGgRkyZNqrT136yMMS653Yhu3bpx4sQJAD7++GO6d+9OXFwcAIGBgcybN4+XX34ZgFdffZU//vGPhIeHA+Dj48PkyZNLLDM7O5vx48cTFRVFdHS04wJWRbfQly5dyrhx4wAYN24czzzzDH379mXatGmEhoYW28tp06YNZ86cIS0tjREjRtC5c2c6d+5MYmJiiXXn5uY61t2xY0e+/vprAOLi4jh79iwdOnRg06ZNxd5z5swZhg8fTkxMDDExMXz33XclPk///v25/fbbiYqKYsWKFYBVymbIkCHExMQQGRnJJ598AsALL7xA+/btiY6OdrrHtWLFCsaMGYMxhq5du3L+/HlOnTpVot2iRYu49957HY/vu+8+OnXqREREBPPnz3c8X7NmTaZPn84dd9xBUlISH330EV26dKFDhw488cQT1vcKMGnSJGJjY4mIiOBPf/pTifXdqLVr1zJgwADq1q1LSEgIAwYMYM2aNSXaGWMce0VZWVk0aXL9Szz37NmTDRs2WFMGXK28GcZdN0BYZe1RZOdnO82mrrB//36pWbOmBAQEyKpVqyptvTejoltDgEtu11O4FZefny8jR46U1atXi4jI008/La+//nqJ9nXq1JGsrCzp2LGjJCcnX3f5zz33nDz11FOOx4VbmUW3Hj/99FMZO3asiIiMHTtWhgwZ4tganjJliixYsEBERLZs2SL9+/cXEZGHHnpINm3aJCIiR48elfDw8BLrnjNnjowbN05ERFJSUqR58+aSk5Mjhw8floiICKfxjho1Sl577TVHn5w/f75YvHl5eZKVlSUiImlpadK6dWux2WyydOnSYlu958+fl/T0dAkLCxObzSYiIpmZmSXWN2TIEMfnEBHp16+fbN26tUS7Fi1ayIULFxyP09PTRcTa84uIiJBz586JiPV39Mknn4iI9fc1dOhQuXz5soiITJo0SRYuXFjs/fn5+dK7d2/ZubPkUYtXX31VYmJiStyefPLJEm1nz54ts2bNcjyeOXOmzJ49u0S7kydPSmRkpDRt2lTq1Kkj27Ztc7zm7e0tnTp1kjvuuEPi4+OLve+uu+4q1rZQRe9ReNxgNgA1wNt4E+gVWGmrvO2221i5ciW+vr706NGj0tZ7s5Mix/4rU05ODh06dODIkSN06tSJAQMGOOK51h7JjeypbNiwgSVLljgeh4SEXPc9DzzwAN7e3gCMHj2amTNnMn78eJYsWcLo0aMdy923b5/jPRcuXODixYvF5vVs3rzZcT2U8PBwbr31Vg4cOEDt2rWvue6vvvqKDz74ALCOmQcHBxd7XUR48cUXSUhIwMvLixMnTnDmzBmioqKYOnUqzz//PEOHDqVnz57k5+fj7+/PxIkTGTJkCEOHDi2xPme/d2f9m5GRUeyzzZ07l/j4eACOHz/OwYMHqVevHt7e3o6zEzdu3Mj27dsdZynm5OTQsKF1BuW///1v5s+fT35+PqdOnWLfvn1ER0cXW+e0adOYNm3aNfuqPJ9j8eLFjBs3jmeffZakpCQee+wx9uzZg5eXF8eOHaNJkyYcOnSIfv36ERUVRevWrQFo2LAhJ0+epFOnTmWKp7w87tAT1v8JAV4BLj+FMiMjo9igUt++fTVJ3CQCAgJITk7m6NGjXL58mTfeeAOAiIgItm3bVqztoUOHqFmzJrVq1SIiIoLt27dfd/nXSjhFn7t6ZnpQ0JUCmN26deOnn34iLS2N5cuXc//99wPWyR5JSUkkJyeTnJzMiRMnSkz+dEXyXbRoEWlpaWzfvp3k5GQaNWpEbm4uYWFhbN++naioKP7rv/6LmTNn4uPjw/fff8+IESNYvnw5gwYNKrG8Zs2acfz4ccfj1NRUp4djfHx8HBWlv/nmGzZs2EBSUhI7d+6kY8eOjj709/d3JFkRYezYsY4++vHHH5kxYwaHDx9mzpw5bNy4kV27djFkyBCn1QFmz57tGFwuepsyZUq5P0fhJZLB+t3m5uZy7tw5AEf7Vq1a0adPH3744QfH+3JzcwkICCixvIrmeYnC/n+UXZDt0tWcPHmS3r17M2zYMNauXevSdamqKzg4mLlz5zJnzhzy8vJ45JFH2Lx5Mxs2bACsrdEpU6bw3HPPAdbW5ksvvcSBAwcA64v7f//3f0ssNy4ujnnz5jkeZ2Za5WgaNWpESkoKNpvNsWXsjDGG4cOH88wzz9CuXTvq1avndLnJyckl3turVy8WLVoEwIEDBzh27Nh1T+/u378/b731FmCN1119llFWVhYNGzbE19eXr7/+mqNHjwLW/1FgYCCPPvooU6dOZceOHWRnZ5OVlcXgwYN5/fXXncY4bNgwPvjgA0SELVu2EBwcTOPGJS9Sdtttt3Ho0CFHDCEhIQQGBrJ//362bNlyzc+ydOlSzp49C1gbhEePHuXChQsEBQURHBzMmTNnWL16tdP3T5s2zZFkit7mzp1bou3AgQNZt24dmZmZZGZmsm7dOgYOHFiiXYsWLdi4cSMAKSkp5Obm0qBBAzIzM/nll18AOHfuHImJibRv397xvgMHDhAREeE0zgpV3mNW7rpRwxqfaPdduxLH4CrKwYMHpWXLlgJI+/btJTU11WXrUiVVtbOeRESGDh0qH3zwgYiI7Nq1S3r37i1hYWHSunVrmTFjhuN4u4jI559/LrfffruEh4dLu3btZOrUqSWWf/HiRRkzZoxERERIdHS0LFu2TESscYlWrVpJ79695Xe/+12xMYpPP/202DK2bt0qgLz//vuO59LS0mTUqFESFRUl7dq1kyeeeKLEunNycmTs2LESGRkpHTp0kK+++kpEpNQxitOnT8uwYcMkMjJSYmJi5LvvvivWT2lpadK1a1fp1KmTTJgwQcLDw+Xw4cOyZs0aiYqKkpiYGImNjZWtW7fKyZMnpXPnzhIVFSWRkZHF4i9ks9lk8uTJ0qpVK4mMjHQ6PiFiHfN/5513REQkNzdXBg0aJFFRUTJy5Ejp3bu3fP3118XiLLRkyRKJiYmRqKgouf322yUpKcnRz+Hh4TJ48GAZPny4/Otf/3K63hvx3nvvSevWraV169aOcSURkf/5n/+RFStWiIh1ptOdd94p0dHREhMT4zirLjExUSIjIyU6OloiIyPl3Xffdbz/9OnT0rlzZ6frrOgxCiNuOgZcXibACF9Abe/aZPXJqvDl79q1i7i4OM6cOUOXLl348ssvHVtrqnKkpKTQrl07d4ehPMCpU6cYM2YM69evd3cole61116jdu3aTJgwocRrzv6HjDHbRSS2POvyvENP9uH3PiF9KnzRiYmJ9OrVizNnztC/f382bNigSUKpKqxx48b85je/qdIT7lylTp06jB07tlLW5XlnPdlTW0XPys7NzWXUqFFkZWVx//338/HHH+sFh5TyAIWDwDeb8ePHV9q6PG+Pwh5xTe+KLR3g7+/Pv//9byZPnswnn3yiSUIppew8b4/CfnpsRV0G9ccff3Sc8dG9e3e6d+9eIctVSqnqwvP2KOyJooFfg1+1GBHhpZdeIiIigs8++6wCAlNKqerpptyjEBGmTZvG3/72N4wxjoktSimlSvK8PQp7xLW8y3ep0fz8fCZOnMjf/vY3fHx8WLx4MY8//ngFBqiqAy0z7t4y4/v376dbt27UqFGDOXPmXLOdiJYZrxTlnYDhrhvNrQl3mzM3O51oUpqcnBwZPny4ABIQEOAo9Kaqlqo24U7LjF+bq8qMnzlzRr7//nt58cUXnRbRK6RlxrXMuHP2iEN8rl9E7Wrjxo0jPj6eOnXqsH79eqc1ZlTVYjYal9xuhJYZr/wy4w0bNqRz5874+vo6/Z0U0jLjlVNm3PPGKOyJItD7xivHFtaaWbp0aYmKkEo5U1BQwMaNGx2zX/fu3VuiUmfr1q3Jzs7mwoUL7Nmzh2efffa6y501axbBwcHs3r0buFLrqTQHDhxgw4YNeHt7O2pBjR8/nv/85z+EhobSqFEjHn74YZ5++ml69OjBsWPHGDhwICkpKcWWU1jgcPfu3ezfv5+4uDgOHDjAypUrGTp0qNPaS1OmTKF3797Ex8dTUFBAdnbxWmv+/v7Ex8dTu3Ztzp07R9euXRk2bBhr1qyhSZMmrFq1CrC+BDMyMoiPj2f//v0YY4olvBuVmJjIP//5T8fjBQsWULduXXJycujcuTMjRoygXr16XLp0icjISGbOnElKSgqvvPIKiYmJ+Pr6MnnyZBYtWsSYMWP461//St26dSkoKKB///7s2rWrxHfF7NmzHbWyiurVq1eJek8nTpygefPmjsfNmjVzbHQUNWPGDOLi4vjHP/7BpUuXHLXEwErssbGx+Pj48MILL3DfffcB4OXlRZs2bdi5c6fLq8d6XqKwbwz6efmVqXlubi7+/v4AxMbGsm/fPnx8PO9j36ykv5YZL3QzlRkvKy0zrmXGnbP3sa8pfZcU4ODBg7Rv356PP/7Y8ZwmCVUWWmb8xlR0mfGy0jLjWmbcOfvpsdebmZ2cnEyPHj04fPgwb7/9tuOPSakboWXGLZVdZrystMy4lhl3ftZTGOK93rtYWeerJSQkSO3atQWQAQMGyMWLF6/ZVlU9Ve2sJxEtM17ZZcZPnTolTZs2lVq1aklwcLA0bdrUcanVorTMuJYZd8rcZoQ3r33setWqVYwcOZLc3FxGjhzJRx99pHWbPIyWGVdlpWXGtcz4NQWK8zOeli5dyn333Udubi4TJ05kyZIlmiSUqsa0zLiWGb+maHF+amtYWBhBQUE88cQTvPzyyy6/prZSyv20zLjreWSiqGmcD2RHR0ezZ88emjVrVskRqYompZyGqpS6NlcMJ3jkoafCRGGz2XjmmWdYsGCB4zVNEp7P39+f9PR0l/zBK1WdiQjp6emOuWMVxSP3KOp61XUU91u4cCEBAQEMHjyYW265xd2hqQrQrFkzUlNTSUtLc3coSnkcf3//Ct9gdmmiMMYMAv6ONfvhXRF5+arXjf31wcDPwDgR2XG95QbmBTJy5EhWrFhBYGAg8fHxmiSqEV9fX1q2bOnuMJRSdi5LFMYYb+ANYACQCmw1xqwUkX1Fmt0NtLXf7gDesv+8tgL44oUvOPLDEUJCQli1ahXdunVzyWdQSinl2jGKLsBPInJIRC4DS4B7r2pzL/CBfT7IFqCOMaZxqUtNhSM/HKFx48YkJCRoklBKKRdzZaJoChwv8jjV/tyNtikuDxo0bcDmzZuJjIysiDiVUkqVwpVjFM7Obbz6NJaytMEY8zhQeBm6X9JOpO0prJ54k6sP6HVcLdoXV2hfXKF9cUXpBb1K4cpEkQo0L/K4GXCyHG0QkfnAfABjzLbyTkOvbrQvrtC+uEL74grtiyuMMduu38o5Vx562gq0Nca0NMb4AQ8CK69qsxIYYyxdgSwROeXCmJRSSt0gl+1RiEi+Meb3wFqs02MXiMheY8xv7a+/DXyJdWrsT1inx1benHSllFJl4tJ5FCLyJVYyKPrc20XuC/C7G1zs/Os3uWloX1yhfXGF9sUV2hdXlLsvPK7MuFJKqcrlkbWelFJKVZ4qmyiMMYOMMT8aY34yxrzg5HVjjJlrf32XMeZ2d8RZGcrQF4/Y+2CXMeY7Y0yMO+KsDNfriyLtOhtjCowxIyszvspUlr4wxvQxxiQbY/YaY76t7BgrSxn+R4KNMZ8bY3ba+6JajocaYxYYY84aY/Zc4/XyfW+W99J4rrxhDX7/f6AV4AfsBNpf1WYwsBprLkZX4D/ujtuNfXEnEGK/f/fN3BdF2n2FNT420t1xu/Hvog6wD2hhf9zQ3XG7sS9eBF6x328AZAB+7o7dBX3RC7gd2HON18v1vVlV9yhcU/7DM123L0TkOxHJtD/cgjUfpToqy98FwJPAMuBsZQZXycrSFw8Dn4nIMQARqa79UZa+EKCWvRBpTaxEkV+5YbqeiCRgfbZrKdf3ZlVNFK4p/+GZbvRzTsDaYqiOrtsXxpimwHDgbaq3svxdhAEhxphvjDHbjTFjKi26ylWWvpgHtMOa0LsbeEpEbJUTXpVSru/Nqno9igor/1ENlPlzGmP6YiWKHi6NyH3K0hevA8+LSEE1v0JeWfrCB+gE9AcCgCRjzBYROeDq4CpZWfpiIJAM9ANaA+uNMZtE5Ga72Ha5vjeraqKosPIf1UCZPqcxJhp4F7hbRNIrKbbKVpa+iAWW2JNEfWCwMSZfRJZXToiVpqz/I+dE5BJwyRiTAMQA1S1RlKUvxgMvi3Wg/idjzGEgHPi+ckKsMsr1vVlVDz1p+Y8rrtsXxpgWwGfAY9Vwa7Go6/aFiLQUkVARCQWWApOrYZKAsv2PrAB6GmN8jDGBWNd6SankOCtDWfriGNaeFcaYRlgF8g5VapRVQ7m+N6vkHoVo+Q+HMvbFdKAe8KZ9SzpfqmEhtDL2xU2hLH0hIinGmDXALsCGdZVJp6dNerIy/l3MAt43xuzGOvzyvIhUu6qyxpjFQB+gvjEmFfgT4Au/7ntTZ2YrpZQqVVU99KSUUqqK0EShlFKqVJoolFJKlUoThVJKqVJpolBKKVUqTRSqSrJXfk0ucgstpW12BazvfWPMYfu6dhhjupVjGe8aY9rb77941Wvf/doY7csp7Jc99mqoda7TvoMxZnBFrFvdvPT0WFUlGWOyRaRmRbctZRnvA1+IyFJjTBwwR0Sif8XyfnVM11uuMWYhcEBE/lpK+3FArIj8vqJjUTcP3aNQHsEYU9MYs9G+tb/bGFOiaqwxprExJqHIFndP+/Nxxpgk+3s/NcZc7ws8AWhjf+8z9mXtMcb8wf5ckDFmlf3aBnuMMaPtz39jjIk1xrwMBNjjWGR/Ldv+85OiW/j2PZkRxhhvY8xsY8xWY10n4IkydEsS9oJuxpguxroWyQ/2n7fZZynPBEbbYxltj32BfT0/OOtHpUpwd/10venN2Q0owCrilgzEY1URqG1/rT7WzNLCPeJs+89ngT/a73sDtextE4Ag+/PPA9OdrO997NeuAB4A/oNVUG83EIRVmnov0BEYAbxT5L3B9p/fYG29O2Iq0qYwxuHAQvt9P6xKngHA48B/25+vAWwDWjqJM7vI5/sUGGR/XBvwsd+/C1hmvz8OmFfk/S8Bj9rv18Gq+xTk7t+33qr2rUqW8FAKyBHG9UsNAAACXklEQVSRDoUPjDG+wEvGmF5Y5SiaAo2A00XesxVYYG+7XESSjTG9gfZAor28iR/Wlrgzs40x/w2kYVXh7Q/Ei1VUD2PMZ0BPYA0wxxjzCtbhqk038LlWA3ONMTWAQUCCiOTYD3dFmytX5AsG2gKHr3p/gDEmGQgFtgPri7RfaIxpi1UN1Pca648Dhhljptof+wMtqJ41oFQF0UShPMUjWFcm6yQiecaYI1hfcg4ikmBPJEOAD40xs4FMYL2IPFSGdUwTkaWFD4wxdzlrJCIHjDGdsGrm/D9jzDoRmVmWDyEiucaYb7DKXo8GFheuDnhSRNZeZxE5ItLBGBMMfAH8DpiLVcvoaxEZbh/4/+Ya7zfACBH5sSzxKgU6RqE8RzBw1p4k+gK3Xt3AGHOrvc07wHtYl4TcAnQ3xhSOOQQaY8LKuM4E4D77e4KwDhttMsY0AX4WkY+AOfb1XC3PvmfjzBKsYmw9sQrZYf85qfA9xpgw+zqdEpEsYAow1f6eYOCE/eVxRZpexDoEV2gt8KSx714ZYzpeax1KFdJEoTzFIiDWGLMNa+9iv5M2fYBkY8wPWOMIfxeRNKwvzsXGmF1YiSO8LCsUkR1YYxffY41ZvCsiPwBRwPf2Q0B/BP7i5O3zgV2Fg9lXWYd1beMNYl26E6xriewDdhhj9gD/5Dp7/PZYdmKV1X4Va+8mEWv8otDXQPvCwWysPQ9fe2x77I+VKpWeHquUUqpUukehlFKqVJoolFJKlUoThVJKqVJpolBKKVUqTRRKKaVKpYlCKaVUqTRRKKWUKpUmCqWUUqX6P89TMsUOYjk3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "\n",
    "vali_proba_df = pd.DataFrame(lr.predict_proba(vali_x))\n",
    "skplt.metrics.plot_roc(vali_y, vali_proba_df,\n",
    "                        plot_micro=False,figsize=(6,6),\n",
    "                        plot_macro=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = 20/np.log(2)\n",
    "A = 600 + B*np.log(1/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([481.86679691])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_score = A - B*lr.intercept_\n",
    "base_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = \"../data/logisitcRegression/ScoreData.csv\"\n",
    "with open(file,\"w\") as fdata:\n",
    "    fdata.write(\"base_score,{}\\n\".format(base_score))\n",
    "for i,col in enumerate(X.columns):\n",
    "    score = woeall[col] * (-B*lr.coef_[0][i])\n",
    "    score.name = \"Score\"\n",
    "    score.index.name = col\n",
    "    score.to_csv(file,header=True,mode=\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
